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class="SearchForm_submit__U8kPR" id="search-submit" data-cy="search-submit"><span class="Icon_root__AjZyv" style="--size:24px"><span class="Icon_icon__4zzsG" style="mask-image:url(https://public.slidesharecdn.com/_next/static/media/search.844a289d.svg);background-color:currentColor"></span><span class="sr-only">Submit Search</span></span></button></form></div><div class="HeaderActions_root__11_ai undefined"></div></header><div class="SlideshowScreen_root__k9j1_ SlideshowScreen_withKeyMoments__RbHLe"><div class="metadata Metadata_root__oCstk" data-cy="metadata"><h1 class="Heading_heading__3MAvZ Heading_h1__3k7S2 title Title_root__svkHQ">Understanding Feature Space in Machine Learning</h1><div class="stats Stats_root__p_BoZ Stats_extendedMetadata__wb62p"><div class="Stats_leftContent__588PR"><time dateTime="2015-09-10 21:44:35 UTC"><span class="skeleton Skeleton_root__U4QqL Skeleton_rounded__BLBq2" style="width:75px;height:24px"></span></time><span class="Text_root__is73U 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src="https://cdn.slidesharecdn.com/profile-photo-AliceZheng3-48x48.jpg?cb=1530851967" alt="Alice Zheng" loading="lazy" decoding="sync"/></div><a class="Link_root__vn3ab Author_link___lVxw ellipsis Link_primary__Iq4CI Link_size-large__W0PAv Link_weight-regular__yPpnB" data-cy="author-link" title="Alice Zheng" href="https://www.slideshare.net/AliceZheng3">Alice Zheng</a><button type="button" class="FollowButton_root__FxpBi Author_follow__Lw4TS FollowButton_follow__d_6u5">Follow</button></div><div class="description Description_root__kt4uq Description_clamped__PaV_1"><div class="Description_wrapper__hYE9_" data-cy="document-description"><p>An explanation of fundamental concepts of features and models in machine learning, building on our geometric intuition of high dimensional spaces.<button type="button" class="Button_root__i1yp0 Button_primary__K25Gq Button_text__ZT_3O Button_small__sqsEx Description_less__BvWbY Description_hidden__a9QZJ" data-testid="button" aria-label="Read 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style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-0" alt="Understanding Feature Space in Machine Learning Alice Zheng, Dato September 9, 2015 1 " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="eager" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-1-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-1-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-1-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-1-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide2" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-1" alt="2 My journey so far Applied machine learning (Data science) Build ML tools Shortage of experts and good tools. " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-2-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-2-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-2-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-2-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide3" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-2" alt="3 Why machine learning? Model data. Make predictions. Build intelligent applications. " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-3-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-3-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-3-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-3-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide4" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-3" alt="4 The machine learning pipeline I fell in love the instant I laid my eyes on that puppy. His big eyes and playful tail, his soft furry paws, … Raw data Features Models Predictions Deploy in production " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-4-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-4-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-4-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-4-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide5" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-4" alt="Feature = numeric representation of raw data " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-5-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-5-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-5-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-5-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide6" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-5" alt="6 Representing natural text It is a puppy and it is extremely cute. What’s important? Phrases? Specific words? Ordering? Subject, object, verb? Classify: puppy or not? Raw Text {“it”:2, “is”:2, “a”:1, “puppy”:1, “and”:1, “extremely”:1, “cute”:1 } Bag of Words " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-6-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-6-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-6-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-6-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide7" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-6" alt="7 Representing natural text It is a puppy and it is extremely cute. Classify: puppy or not? Raw Text Bag of Words it 2 they 0 I 1 am 0 how 0 puppy 1 and 1 cat 0 aardvark 0 cute 1 extremely 1 … … Sparse vector representation " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-7-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-7-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-7-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-7-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide8" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-7" alt="8 Representing images Image source: “Recognizing and learning object categories,” Li Fei-Fei, Rob Fergus, Anthony Torralba, ICCV 2005—2009. Raw image: millions of RGB triplets, one for each pixel Classify: person or animal? Raw Image Bag of Visual Words " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-8-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-8-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-8-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-8-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide9" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-8" alt="9 Representing images Classify: person or animal? Raw Image Deep learning features 3.29 -15 -5.24 48.3 1.36 47.1 - 1.92 36.5 2.83 95.4 -19 -89 5.09 37.8 Dense vector representation " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-9-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-9-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-9-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-9-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide10" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-9" alt="10 Feature space in machine learning • Raw data  high dimensional vectors • Collection of data points  point cloud in feature space • Model = geometric summary of point cloud • Feature engineering = creating features of the appropriate granularity for the task " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-10-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-10-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-10-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-10-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide11" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-10" alt="Crudely speaking, mathematicians fall into two categories: the algebraists, who find it easiest to reduce all problems to sets of numbers and variables, and the geometers, who understand the world through shapes. -- Masha Gessen, “Perfect Rigor” " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-11-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-11-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-11-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-11-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide12" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-11" alt="12 Algebra vs. Geometry a b c a2 + b2 = c2 Algebra Geometry Pythagorean Theorem (Euclidean space) " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-12-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-12-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-12-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-12-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide13" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-12" alt="13 Visualizing a sphere in 2D x2 + y2 = 1 a b c Pythagorean theorem: a2 + b2 = c2 x y 1 1 " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-13-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-13-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-13-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-13-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide14" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-13" alt="14 Visualizing a sphere in 3D x2 + y2 + z2 = 1 x y z 1 1 1 " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-14-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-14-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-14-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-14-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide15" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-14" alt="15 Visualizing a sphere in 4D x2 + y2 + z2 + t2 = 1 x y z 1 1 1 " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-15-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-15-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-15-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-15-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide16" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-15" alt="16 Why are we looking at spheres? = = = = Poincaré Conjecture: All physical objects without holes is “equivalent” to a sphere. " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-16-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-16-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-16-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-16-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide17" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-16" alt="17 The power of higher dimensions • A sphere in 4D can model the birth and death process of physical objects • Point clouds = approximate geometric shapes • High dimensional features can model many things " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-17-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-17-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-17-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-17-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide18" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-17" alt="Visualizing Feature Space " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-18-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-18-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-18-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-18-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide19" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-18" alt="19 The challenge of high dimension geometry • Feature space can have hundreds to millions of dimensions • In high dimensions, our geometric imagination is limited - Algebra comes to our aid " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-19-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-19-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-19-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-19-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide20" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-19" alt="20 Visualizing bag-of-words puppy cute 1 1 I have a puppy and it is extremely cute I have a puppy and it is extremely cute it 1 they 0 I 1 am 0 how 0 puppy 1 and 1 cat 0 aardvark 0 zebra 0 cute 1 extremely 1 … … " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-20-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-20-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-20-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-20-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide21" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-20" alt="21 Visualizing bag-of-words puppy cute 1 1 1 extremely I have a puppy and it is extremely cute I have an extremely cute cat I have a cute puppy " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-21-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-21-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-21-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-21-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide22" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-21" alt="22 Document point cloud word 1 word 2 " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-22-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-22-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-22-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-22-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide23" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-22" alt="23 What is a model? • Model = mathematical “summary” of data • What’s a summary? - A geometric shape " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-23-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-23-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-23-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-23-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide24" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-23" alt="24 Classification model Feature 2 Feature 1 Decide between two classes " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-24-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-24-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-24-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-24-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide25" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-24" alt="25 Clustering model Feature 2 Feature 1 Group data points tightly " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-25-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-25-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-25-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-25-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide26" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-25" alt="26 Regression model Target Feature Fit the target values " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-26-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-26-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-26-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-26-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide27" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-26" alt="Visualizing Feature Engineering " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-27-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-27-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-27-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-27-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide28" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-27" alt="28 When does bag-of-words fail? puppy cat 2 1 1 have I have a puppy I have a cat I have a kitten Task: find a surface that separates documents about dogs vs. cats Problem: the word “have” adds fluff instead of information I have a dog and I have a pen 1 " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-28-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-28-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-28-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-28-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide29" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-28" alt="29 Improving on bag-of-words • Idea: “normalize” word counts so that popular words are discounted • Term frequency (tf) = Number of times a terms appears in a document • Inverse document frequency of word (idf) = • N = total number of documents • Tf-idf count = tf x idf " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-29-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-29-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-29-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-29-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide30" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-29" alt="30 From BOW to tf-idf puppy cat 2 1 1 have I have a puppy I have a cat I have a kitten idf(puppy) = log 4 idf(cat) = log 4 idf(have) = log 1 = 0 I have a dog and I have a pen 1 " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-30-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-30-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-30-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-30-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide31" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-30" alt="31 From BOW to tf-idf puppy cat1 have tfidf(puppy) = log 4 tfidf(cat) = log 4 tfidf(have) = 0 I have a dog and I have a pen, I have a kitten 1 log 4 log 4 I have a cat I have a puppy Decision surface Tf-idf flattens uninformative dimensions in the BOW point cloud " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-31-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-31-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-31-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-31-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide32" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-31" alt="32 Entry points of feature engineering • Start from data and task - What’s the best text representation for classification? • Start from modeling method - What kind of features does k-means assume? - What does linear regression assume about the data? " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-32-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-32-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-32-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-32-320.jpg" sizes="100vw"/></div></div></div><div><div id="slide33" class="VerticalSlide_root__jU_9r slide-item" style="aspect-ratio:960 / 540" data-cy="slide-container"><div class="VerticalSlideImage_root__64KSA"><img id="slide-image-32" alt="33 That’s not all, folks! • There’s a lot more to feature engineering: - Feature normalization - Feature transformations - “Regularizing” models - Learning the right features • Dato is hiring! jobs@dato.com alicez@dato.com @RainyData " class="vertical-slide-image VerticalSlideImage_image__VtE4p" data-testid="vertical-slide-image" loading="lazy" srcSet="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-33-320.jpg 320w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-33-638.jpg 638w, https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/75/Understanding-Feature-Space-in-Machine-Learning-33-2048.jpg 2048w" src="https://image.slidesharecdn.com/understanding-feature-space-150910214435-lva1-app6892/85/Understanding-Feature-Space-in-Machine-Learning-33-320.jpg" sizes="100vw"/></div></div></div></div></div></div><div class="RelatedContent_root__29Np1"><div class="RelatedContent_wrapper__riU7l"><h2 class="Heading_heading__3MAvZ Heading_h2__f9yvs RelatedContent_title__QUhpL">More Related Content</h2><div></div><div></div><div id="between-recs-ad-1-container" class="freestar-ad-container FreestarAdContainer_root__qPPC_" style="--fallback-aspect-ratio:undefined / undefined"><div><div class="" id="between-recs-ad-1"></div></div></div><div></div><div id="between-recs-ad-2-container" class="freestar-ad-container FreestarAdContainer_root__qPPC_" style="--fallback-aspect-ratio:undefined / undefined"><div><div class="" id="between-recs-ad-2"></div></div></div><div></div></div></div><div class="Transcript_root__Vrf6Q"><h2 class="Transcript_title__YgAka"><span class="Icon_root__AjZyv" style="--size:24px"><span class="Icon_icon__4zzsG" style="mask-image:url(https://public.slidesharecdn.com/_next/static/media/file.5db1ba24.svg);background-color:currentColor"></span><span class="sr-only"></span></span>Understanding Feature Space in Machine Learning</h2><div><ul class="Transcript_list__faItj"><div><li>1. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#1">Understanding Feature Space in Machine </a> Learning Alice Zheng, Dato September 9, 2015 1 </li></div><div><li>2. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#2">2 My journey so </a> far Applied machine learning (Data science) Build ML tools Shortage of experts and good tools. </li></div><div><li>3. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#3">3 Why machine learning? Model </a> data. Make predictions. Build intelligent applications. </li></div><div><li>4. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#4">4 The machine learning </a> pipeline I fell in love the instant I laid my eyes on that puppy. His big eyes and playful tail, his soft furry paws, … Raw data Features Models Predictions Deploy in production </li></div><div><li>5. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#5">Feature = numeric </a> representation of raw data </li></div><div><li>6. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#6">6 Representing natural text It </a> is a puppy and it is extremely cute. What’s important? Phrases? Specific words? Ordering? Subject, object, verb? Classify: puppy or not? Raw Text {“it”:2, “is”:2, “a”:1, “puppy”:1, “and”:1, “extremely”:1, “cute”:1 } Bag of Words </li></div><div><li>7. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#7">7 Representing natural text It </a> is a puppy and it is extremely cute. Classify: puppy or not? Raw Text Bag of Words it 2 they 0 I 1 am 0 how 0 puppy 1 and 1 cat 0 aardvark 0 cute 1 extremely 1 … … Sparse vector representation </li></div><div><li>8. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#8">8 Representing images Image source: </a> “Recognizing and learning object categories,” Li Fei-Fei, Rob Fergus, Anthony Torralba, ICCV 2005—2009. Raw image: millions of RGB triplets, one for each pixel Classify: person or animal? Raw Image Bag of Visual Words </li></div><div><li>9. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#9">9 Representing images Classify: person or </a> animal? Raw Image Deep learning features 3.29 -15 -5.24 48.3 1.36 47.1 - 1.92 36.5 2.83 95.4 -19 -89 5.09 37.8 Dense vector representation </li></div><div><li>10. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#10">10 Feature space in </a> machine learning • Raw data  high dimensional vectors • Collection of data points  point cloud in feature space • Model = geometric summary of point cloud • Feature engineering = creating features of the appropriate granularity for the task </li></div><div><li>11. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#11">Crudely speaking, mathematicians </a> fall into two categories: the algebraists, who find it easiest to reduce all problems to sets of numbers and variables, and the geometers, who understand the world through shapes. -- Masha Gessen, “Perfect Rigor” </li></div><div><li>12. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#12">12 Algebra vs. Geometry a b c a2 </a> + b2 = c2 Algebra Geometry Pythagorean Theorem (Euclidean space) </li></div><div><li>13. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#13">13 Visualizing a sphere </a> in 2D x2 + y2 = 1 a b c Pythagorean theorem: a2 + b2 = c2 x y 1 1 </li></div><div><li>14. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#14">14 Visualizing a sphere </a> in 3D x2 + y2 + z2 = 1 x y z 1 1 1 </li></div><div><li>15. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#15">15 Visualizing a sphere </a> in 4D x2 + y2 + z2 + t2 = 1 x y z 1 1 1 </li></div><div><li>16. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#16">16 Why are we </a> looking at spheres? = = = = Poincaré Conjecture: All physical objects without holes is “equivalent” to a sphere. </li></div><div><li>17. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#17">17 The power of </a> higher dimensions • A sphere in 4D can model the birth and death process of physical objects • Point clouds = approximate geometric shapes • High dimensional features can model many things </li></div><div><li>18. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#18">Visualizing Feature Space </a> </li></div><div><li>19. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#19">19 The challenge of </a> high dimension geometry • Feature space can have hundreds to millions of dimensions • In high dimensions, our geometric imagination is limited - Algebra comes to our aid </li></div><div><li>20. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#20">20 Visualizing bag-of-words puppy cute 1 1 I have </a> a puppy and it is extremely cute I have a puppy and it is extremely cute it 1 they 0 I 1 am 0 how 0 puppy 1 and 1 cat 0 aardvark 0 zebra 0 cute 1 extremely 1 … … </li></div><div><li>21. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#21">21 Visualizing bag-of-words puppy cute 1 1 1 extremely I have </a> a puppy and it is extremely cute I have an extremely cute cat I have a cute puppy </li></div><div><li>22. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#22">22 Document point cloud word </a> 1 word 2 </li></div><div><li>23. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#23">23 What is a </a> model? • Model = mathematical “summary” of data • What’s a summary? - A geometric shape </li></div><div><li>24. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#24">24 Classification model Feature 2 Feature </a> 1 Decide between two classes </li></div><div><li>25. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#25">25 Clustering model Feature 2 Feature </a> 1 Group data points tightly </li></div><div><li>26. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#26">26 Regression model Target Feature Fit the </a> target values </li></div><div><li>27. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#27">Visualizing Feature Engineering </a> </li></div><div><li>28. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#28">28 When does bag-of-words </a> fail? puppy cat 2 1 1 have I have a puppy I have a cat I have a kitten Task: find a surface that separates documents about dogs vs. cats Problem: the word “have” adds fluff instead of information I have a dog and I have a pen 1 </li></div><div><li>29. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#29">29 Improving on bag-of-words • </a> Idea: “normalize” word counts so that popular words are discounted • Term frequency (tf) = Number of times a terms appears in a document • Inverse document frequency of word (idf) = • N = total number of documents • Tf-idf count = tf x idf </li></div><div><li>30. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#30">30 From BOW to </a> tf-idf puppy cat 2 1 1 have I have a puppy I have a cat I have a kitten idf(puppy) = log 4 idf(cat) = log 4 idf(have) = log 1 = 0 I have a dog and I have a pen 1 </li></div><div><li>31. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#31">31 From BOW to </a> tf-idf puppy cat1 have tfidf(puppy) = log 4 tfidf(cat) = log 4 tfidf(have) = 0 I have a dog and I have a pen, I have a kitten 1 log 4 log 4 I have a cat I have a puppy Decision surface Tf-idf flattens uninformative dimensions in the BOW point cloud </li></div><div><li>32. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#32">32 Entry points of </a> feature engineering • Start from data and task - What’s the best text representation for classification? • Start from modeling method - What kind of features does k-means assume? - What does linear regression assume about the data? </li></div><div><li>33. <a class="Transcript_link__MLbGS" href="https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207#33">33 That’s not all, </a> folks! • There’s a lot more to feature engineering: - Feature normalization - Feature transformations - “Regularizing” models - Learning the right features • Dato is hiring! jobs@dato.com alicez@dato.com @RainyData </li></div></ul></div></div><div class="EditorsNotes_root__3PcDF"><h3 class="Heading_heading__3MAvZ Heading_h3__f1djd EditorsNotes_heading__XR9E6">Editor's Notes</h3><ol class="EditorsNotes_list__NcG5Y"><li class="EditorsNotes_item__ebBbj">Features sit between raw data and model. 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Des plateformes au service de l’information scientifique comme l’équipement d’excellence OpenEdition.org sont une autre brique essentielle pour la préservation et l’accès aux « Big Digital Humanities » mais aussi pour favoriser la reproductibilité et la compréhension des expérimentations et des résultats obtenus.","tags":["digital humanities","dariah","clarin"],"url":"https://www.slideshare.net/slideshow/infrastructures-et-recommandations-pour-les-humanits-numriques-big-data-et-shs/55575185","userLogin":"PatriceBellot","userName":"Patrice Bellot - Aix-Marseille Université / CNRS (LIS, INS2I)","viewCount":620},{"algorithmId":"3","displayTitle":"Introduction to Search Systems - ScaleConf Colombia 2017","isSavedByCurrentUser":false,"pageCount":74,"score":0.3387,"slideshowId":"73641635","sourceName":"cm_text","strippedTitle":"introduction-to-search-systems-scaleconf-colombia-2017","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/slides-170325222550-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"Often when a new user arrives on your website, the first place they go to find information is the search box! Whether they are searching for hotels on your travel site, products on your e-commerce site, or friends to connect with on your social media site, it is important to have fast, effective search in order to engage the user.","tags":["engineering","search"],"url":"https://www.slideshare.net/slideshow/introduction-to-search-systems-scaleconf-colombia-2017/73641635","userLogin":"ToriaGibbs","userName":"Toria Gibbs","viewCount":1282},{"algorithmId":"3","displayTitle":"CSCE181 Big ideas in NLP","isSavedByCurrentUser":false,"pageCount":76,"score":0.3324,"slideshowId":"254230027","sourceName":"cm_text","strippedTitle":"csce181-big-ideas-in-nlp","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/csce181nlpseminarsophomores-221116023352-130f74e5-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"Introductory seminar on NLP for CS sophomores. Presented to Texas A\u0026M's Fall 2022 CSCE181 class. Slides are a bit redundant due to compatibility issues :\\","tags":["nlp"],"url":"https://www.slideshare.net/slideshow/csce181-big-ideas-in-nlp/254230027","userLogin":"InsuJeong","userName":"Insoo Chung","viewCount":113},{"algorithmId":"3","displayTitle":"Peter Norvig - NYC Machine Learning 2013","isSavedByCurrentUser":false,"pageCount":85,"score":0.33,"slideshowId":"23869704","sourceName":"cm_text","strippedTitle":"norvig-nycml2013","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/norvig-nyc-ml-2013-130703131408-phpapp01-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"The document discusses learning programming through MOOCs and machine learning. It provides data on a MOOC with over 160,000 students from 209 countries. It analyzes student error messages, submissions, and interactions to improve programming instructions. However, programming languages can be ambiguous and students struggle with different concepts. The document advocates for mastery learning through one-on-one tutoring and continual course improvements using data and machine learning.","tags":["machine learning"],"url":"https://www.slideshare.net/slideshow/norvig-nycml2013/23869704","userLogin":"scovetta","userName":"Michael Scovetta","viewCount":1510},{"algorithmId":"3","displayTitle":"syntherella feedback synthesizer","isSavedByCurrentUser":false,"pageCount":19,"score":0.3267,"slideshowId":"8567855","sourceName":"cm_text","strippedTitle":"syntherella-feedback-synthe","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/gisynth-110711162911-phpapp01-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"This presentation describes a mechanism for synthesizing meaningful concise descriptions for exploring virtual worlds using a screenreader. ","tags":["virtual worlds","blind","accessibility"],"url":"https://www.slideshare.net/slideshow/syntherella-feedback-synthe/8567855","userLogin":"eelkefolmer","userName":"Eelke Folmer","viewCount":278},{"algorithmId":"3","displayTitle":"DL Classe 0 - You can do it","isSavedByCurrentUser":false,"pageCount":118,"score":0.3249,"slideshowId":"65599899","sourceName":"cm_text","strippedTitle":"dl-classe-0-you-can-do-it","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/52218a7f-913c-40ab-bc18-85cde0c9f8c3-160901171059-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":" Here are some key terms that are similar to \"champagne\":\n\n- Sparkling wines\n- French champagne \n- Cognac\n- Rosé \n- White wine\n- Sparkling wine\n- Wine\n- Burgundy \n- Bordeaux\n- Cava\n- Prosecco\n\nSome specific champagne brands that are similar terms include Moët, Veuve Clicquot, Dom Pérignon, Taittinger, and Bollinger. Grape varieties used in champagne production like Chardonnay and Pinot Noir could also be considered similar terms.","tags":[],"url":"https://www.slideshare.net/GregoryRenard/dl-classe-0-you-can-do-it","userLogin":"GregoryRenard","userName":"Gregory Renard","viewCount":350},{"algorithmId":"3","displayTitle":"Deep Learning Class #0 - You Can Do It","isSavedByCurrentUser":false,"pageCount":118,"score":0.3249,"slideshowId":"61554750","sourceName":"cm_text","strippedTitle":"deep-learning-class-0-by-louis-monier-gregory-renard","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/deeplearningclass0-louismonier-160501185036-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"\"You Can Do It\" by Louis Monier (Altavista Co-Founder \u0026 CTO) \u0026 Gregory Renard (CTO \u0026 Artificial Intelligence Lead Architect at Xbrain) for Deep Learning keynote #0 at Holberton School (http://www.meetup.com/Holberton-School/events/228364522/) \r\n\r\nIf you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/\r\n","tags":["course","neurons","deepmind"],"url":"https://www.slideshare.net/slideshow/deep-learning-class-0-by-louis-monier-gregory-renard/61554750","userLogin":"holbertonschool","userName":"Holberton School","viewCount":2976},{"algorithmId":"3","displayTitle":"Word2vec ultimate beginner","isSavedByCurrentUser":false,"pageCount":39,"score":0.3211,"slideshowId":"76616396","sourceName":"cm_text","strippedTitle":"word2vec-ultimate-beginner","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/word2vecsungminvt17semantic-170603144702-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"word2vec beginner.\r\nvector space, distributional semantics, word embedding, vector representation for word, word vector representation, sparse and dense representation, vector representation, Google word2vec, tensorflow","tags":["tensorflow","word vector representation","vector representation"],"url":"https://www.slideshare.net/slideshow/word2vec-ultimate-beginner/76616396","userLogin":"SUNGMINYANG6","userName":"Sungmin Yang","viewCount":847},{"algorithmId":"3","displayTitle":"Edutalk f2013","isSavedByCurrentUser":false,"pageCount":61,"score":0.3144,"slideshowId":"26955510","sourceName":"cm_text","strippedTitle":"edutalk-f2013","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/edutalk-f2013-131007172343-phpapp02-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"1. The document discusses educational theory and concepts relevant to learning at hacker schools. \n2. It promotes three main ideas: that learning is designable like coding, individual brains learn differently, and learning is not an isolated process but relies on community and collaboration.\n3. Various learning theories are covered briefly, including cognitive apprenticeship and legitimate peripheral participation within a community of practice. Motivation, mindset, and overcoming challenges are also addressed.","tags":[],"url":"https://www.slideshare.net/slideshow/edutalk-f2013/26955510","userLogin":"mchua","userName":"Mel Chua","viewCount":4142},{"algorithmId":"3","displayTitle":"Collegeteaching102","isSavedByCurrentUser":false,"pageCount":77,"score":0.3137,"slideshowId":"13216238","sourceName":"cm_text","strippedTitle":"collegeteaching102","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/collegeteaching102-120605211414-phpapp01-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"This document provides an overview of strategies for effective college teaching, including facilitating discussions, delivering lectures, assessing student comprehension through testing, and incorporating educational technologies. A variety of specific techniques are presented for each teaching method, with examples and suggestions for implementation. The goal is to help educators engage students and promote learning.","tags":[],"url":"https://www.slideshare.net/slideshow/collegeteaching102/13216238","userLogin":"jonidunlap","userName":"Joanna Dunlap","viewCount":494},{"algorithmId":"3","displayTitle":"Using binary classifiers","isSavedByCurrentUser":false,"pageCount":39,"score":0.3135,"slideshowId":"3858886","sourceName":"cm_text","strippedTitle":"using-binary-classifiers","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/using-binary-classifiers3522-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"The document provides an overview of machine learning and discusses various concepts related to applying machine learning to real-world problems. It covers topics such as feature extraction, encoding input data, classification vs regression, evaluating model performance, and challenges like overfitting and underfitting models to data. Examples are given for different types of learning problems, including text classification, sentiment analysis, and predicting stock prices.","tags":["pattern recognition","machine learning"],"url":"https://www.slideshare.net/slideshow/using-binary-classifiers/3858886","userLogin":"butest","userName":"butest","viewCount":623},{"algorithmId":"3","displayTitle":"Translation to QL Part 1","isSavedByCurrentUser":false,"pageCount":28,"score":0.3129,"slideshowId":"80752817","sourceName":"cm_text","strippedTitle":"translation-to-ql-part-1","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/transltoqlv2-171012201238-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"This document introduces the basics of translating statements from natural language to the formal language of Quantified Logic (QL). It explains that QL uses constants to represent singular terms, predicates represented by capital letters, and variables represented by lowercase letters. Quantifiers like \"for all\" and \"there exists\" are used to represent statements about properties of individuals or groups. To translate a statement to QL, one must identify whether quantifiers are used, what the universe of discourse is, any singular terms, and the relevant predicates to determine the proper representation using constants, predicates, variables, quantifiers and logical connectives.","tags":[],"url":"https://www.slideshare.net/slideshow/translation-to-ql-part-1/80752817","userLogin":"NatKarablina","userName":"Nat Karablina","viewCount":352}],"moreFromUser":[],"featured":null,"latest":[{"algorithmId":"4","displayTitle":"Stabilising forces in macromolecules. .","isSavedByCurrentUser":false,"pageCount":24,"score":0,"slideshowId":"273566331","sourceName":"LATEST","strippedTitle":"stabilising-forces-in-macromolecules","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/stabilisingforcesinmacromoleculescopycopy-241124220231-724eb1ba-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"Stabilizing forces in macromolecules are essential for maintaining their structure and function. These forces can be broadly categorized into several types:\n\n### 1. **Covalent Bonds**\n - **Peptide Bonds**: In proteins, covalent bonds link amino acids together, forming a polypeptide chain.\n - **Glycosidic Bonds**: In carbohydrates, these bonds connect sugar units.\n - **Phosphodiester Bonds**: In nucleic acids, these bonds link nucleotides together in DNA and RNA.\n\n### 2. **Non-Covalent Interactions**\n - **Hydrogen Bonds**: These are weak interactions between a hydrogen atom covalently bonded to an electronegative atom and another electronegative atom. They are crucial in stabilizing secondary structures in proteins (e.g., α-helices and β-sheets) and base pairing in nucleic acids.\n - **Ionic Interactions**: Also known as electrostatic interactions, these occur between charged groups (e.g., between oppositely charged amino acid side chains).\n - **Van der Waals Forces**: These are weak, non-specific interactions that occur between all atoms, contributing to the overall stability of macromolecular structures.\n - **Hydrophobic Interactions**: Non-polar side chains in proteins tend to cluster together to avoid contact with water, driving the folding of proteins and stabilizing their three-dimensional structure.\n\n### 3. **Structural Elements**\n - **Disulfide Bonds**: Covalent bonds formed between cysteine residues, providing additional stability to the folded structure of proteins, particularly in extracellular environments.\n - **Metal Ion Coordination**: Certain macromolecules, especially proteins, can be stabilized by the binding of metal ions (e.g., zinc, iron), which can facilitate structural integrity and function.\n\n### 4. **Macromolecular Assembly**\n - **Quaternary Structure Stabilization**: In proteins with multiple subunits, interactions between these subunits (through non-covalent forces) are critical for maintaining the functional structure.\n - **Polymerization Forces**: In nucleic acids and polysaccharides, the formation of larger complexes or chains is stabilized by the aforementioned interactions.\n\n### 5. **Environmental Factors**\n - **pH and Ionic Strength**: Changes in pH and ionic concentration can affect ionic interactions and hydrogen bonding, influencing the stability of macromolecules.\n - **Temperature**: Higher temperatures can disrupt stabilizing interactions, leading to denaturation.\n\n### Summary\nThe stability of macromolecules is a result of a combination of covalent bonds and various non-covalent interactions. These forces enable macromolecules to maintain their structural integrity and perform their biological functions effectively. Understanding these stabilizing forces is crucial in fields such as biochemistry, molecular biology, and materials science.","tags":["biotechnology"],"url":"https://www.slideshare.net/slideshow/stabilising-forces-in-macromolecules/273566331","userLogin":"nesmasamad11","userName":"nesmasamad11","viewCount":13},{"algorithmId":"4","displayTitle":"KE Energy and PE Energy PowerPoint Presentation","isSavedByCurrentUser":false,"pageCount":35,"score":0,"slideshowId":"273414079","sourceName":"LATEST","strippedTitle":"ke-energy-and-pe-energy-powerpoint-presentation","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/keenergyandpeenergy-241119000900-45213bc9-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"Kinetic and Potential Energy","tags":[],"url":"https://www.slideshare.net/slideshow/ke-energy-and-pe-energy-powerpoint-presentation/273414079","userLogin":"TamaraCarey1","userName":"TamaraCarey1","viewCount":10},{"algorithmId":"4","displayTitle":"principles of hydroponics in vegetable production by sejal","isSavedByCurrentUser":false,"pageCount":27,"score":0,"slideshowId":"273472531","sourceName":"LATEST","strippedTitle":"principles-of-hydroponics-in-vegetable-production-by-sejal","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/hydroponics-241120160818-2bfc72a8-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"Hydroponics is a method of growing plants without soil, using nutrient-rich water to deliver essential minerals directly to plant roots. This approach is particularly beneficial for areas with poor soil quality or limited land space. Below are the key principles of hydroponics:","tags":[],"url":"https://www.slideshare.net/slideshow/principles-of-hydroponics-in-vegetable-production-by-sejal/273472531","userLogin":"u8787094","userName":"u8787094","viewCount":38},{"algorithmId":"4","displayTitle":"\" Microbial growth : Phases \u0026 Factors \".","isSavedByCurrentUser":false,"pageCount":16,"score":0,"slideshowId":"273587083","sourceName":"LATEST","strippedTitle":"microbial-growth-phases-factors","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/microbialgrowth-241125143139-be7754e9-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"*\"Unraveling the Dynamics of Microbial Growth\"*\n\nExplore the fascinating world of microorganisms and discover the intricacies of their growth patterns. This presentation delves into the dynamics of microbial growth, covering topics such as:\n\n- Growth phases: Lag, exponential, stationary, and decline\n- Environmental factors influencing growth: temperature, pH, nutrients, and more\n- Kinetics of microbial growth: growth rates, generation times, and yield coefficients.","tags":["microbial growth","kinetics","science"],"url":"https://www.slideshare.net/slideshow/microbial-growth-phases-factors/273587083","userLogin":"nandhinis547068","userName":"Nandhini S","viewCount":12},{"algorithmId":"4","displayTitle":"pilot plant scale up for solids general considerationations","isSavedByCurrentUser":false,"pageCount":20,"score":0,"slideshowId":"273541507","sourceName":"LATEST","strippedTitle":"pilot-plant-scale-up-for-solids-general-considerationations","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/generalconsiderationsforsolidsgcp-241123093505-bee1faf1-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"reporting responsibility\npersonels\nrawmaterials","tags":["gebe","solids pilot plant scaleup"],"url":"https://www.slideshare.net/slideshow/pilot-plant-scale-up-for-solids-general-considerationations/273541507","userLogin":"NagaChandrikaPallam","userName":"NagaChandrikaPallam","viewCount":31},{"algorithmId":"4","displayTitle":"Phase, freq and spectra in seismic interpretation.pptx","isSavedByCurrentUser":false,"pageCount":24,"score":0,"slideshowId":"273433005","sourceName":"LATEST","strippedTitle":"phase-freq-and-spectra-in-seismic-interpretation-pptx","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/phasefreqandspectrainseismicinterpretation-241119121915-9f2b7f29-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"Phase, freq and spectra in seismic interpretation.pptx\nBy Sharad Kumar Mishra\nGeophysicist","tags":["phase","frequency","seismic facies"],"url":"https://www.slideshare.net/slideshow/phase-freq-and-spectra-in-seismic-interpretation-pptx/273433005","userLogin":"sharadmishra16","userName":"SHARAD KUMAR MISHRA","viewCount":85},{"algorithmId":"4","displayTitle":"Modernizing Kazakh Writing: A Linguistic Revolution","isSavedByCurrentUser":false,"pageCount":12,"score":0,"slideshowId":"273524281","sourceName":"LATEST","strippedTitle":"modernizing-kazakh-writing-a-linguistic-revolution","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/igmin148-241122121301-834c8728-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"Th e article provides information about modern problems of writing the Kazakh language, the importance of its role and development in the context of mass\ndigitization using artifi cial intelligence technologies and computational linguistics methods. Th e incorrectness of the current alphabet of the Kazakh language based\non the Cyrillic alphabet is proved in connection with the inclusion of Cyrillic letters in it, denoting phonemes that are not included in its sound structure. Th e\nnecessity of reforming the Kazakh writing by replacing the incorrect alphabet is substantiated. Errors and contradictions are shown in the approved version of the\nKazakh alphabet based on the Latin alphabet, as well as the alphabet proposed as a replacement for the approved one, in which some previous errors are repeated.\nIn both cases, no analysis and clarifi cation of the sound system of the Kazakh language, which is the basis of any alphabet, is carried out. In this study, to clarify\nthe sound system of the Kazakh language, experiments were carried out to determine the articulation and acoustic features of Kazakh sounds with the help the\ncomputer programs used for many natural languages. In the articulation analysis, special attention was paid to vowels, which give rise to various contradictions in\nthe Kazakh letter. It is proposed to use a new classifi cation of vowels according to four binary features, rather than the traditional classifi cation according to three\nbinary features. Acoustic analysis uses the method of formant analysis, which is aimed at identifying certain formants in the spectrogram. Th e formant is obtained\nusing a spectrograph. Quantitatively, the formants correspond to the maxima in the speech spectrum and usually appear on spectrograms as horizontal bands. Aft er\ndetermining the composition and classifi cation of the sound system of the Kazakh language, two variants of the alphabet based on the Latin alphabet are proposed:\nthe fi rst one is based on the Turkish alphabet using diacritical marks; the second is based on the English alphabet using digraphs. Th e second option off ers ways to\nsolve problems that arise when using digraphs. In conclusion, information is provided on the ongoing and ongoing work in Kazakhstan related to the creation of\nsmart systems in the Kazakh language based on the methods and technologies of artifi cial intelligence and computational linguistics, the results of which are refl ected\nin the list of sources.","tags":["allophone","diacrític","digraph"],"url":"https://www.slideshare.net/slideshow/modernizing-kazakh-writing-a-linguistic-revolution/273524281","userLogin":"igminresearch","userName":"IgMin Publications Inc.","viewCount":40},{"algorithmId":"4","displayTitle":"Introduction to Biomanufacturing process","isSavedByCurrentUser":false,"pageCount":18,"score":0,"slideshowId":"273506116","sourceName":"LATEST","strippedTitle":"introduction-to-biomanufacturing-process","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/1-chapter-1-updated-241121193102-10ccba8f-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"BIOPHARMACEUTICAL MANUFACTURING (BIOMANUFACTURING)\nBiomanufacturing, a specialization within biotechnology, is an advanced-technology\nmanufacturing industry responsible for making biopharmaceuticals (biologics).\nBiopharmaceuticals are any biotechnology-based therapeutics that structurally mimic\ncomponents found in a living organism. These can include:\n hormones\n growth factors\n blood proteins\n clotting factors\n enzymes\n antibodies\n DNA and RNA\n stem cells\nThe application of biopharmaceuticals in health and medicine are numerous:\n therapeutic proteins for treatment of disease\n vaccines to prevent disease\n protein or DNA-based diagnostics\n regenerative medicine technology\n gene therapy\nProduction of the first biopharmaceutical\nModern biomanufacturing began when recombinant human insulin was first commercially\nproduced and marketed in the United States in 1982. The effort leading up to this landmark\nevent began in the early 1970s when research scientists developed protocols to construct DNA\nvectors. The scientists cut out pieces of DNA then pasted them into small circular DNA\nmolecules known as plasmid DNA to create a new piece of DNA (recombinant DNA). This\nrecombinant DNA could be inserted into the bacterium Escherichia coli by the process of\ntransformation. If one of the pieces of the new DNA included a gene that produced an enzyme\nthat broke down a particular antibiotic, the bacterium containing the introduced gene would be\nresistant to that antibiotic. This provides a means of selection for the bacteria that take up the\nplasmid since the bacteria can now grow in a medium containing it the antibiotic","tags":["biomanufacturing","introduction","therapeutic proteins for treat"],"url":"https://www.slideshare.net/slideshow/introduction-to-biomanufacturing-process/273506116","userLogin":"gaithsamerali","userName":"gaithsamerali","viewCount":43},{"algorithmId":"4","displayTitle":"SM-Chordates, protochordates.ppt core paper","isSavedByCurrentUser":false,"pageCount":45,"score":0,"slideshowId":"273418295","sourceName":"LATEST","strippedTitle":"sm-chordates-protochordates-ppt-core-paper","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/sm-chordatesprotochordates-241119031037-1daeddf3-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"Chordates, protochordates","tags":[],"url":"https://www.slideshare.net/slideshow/sm-chordates-protochordates-ppt-core-paper/273418295","userLogin":"rakhibhrin","userName":"rakhibhrin","viewCount":104},{"algorithmId":"4","displayTitle":"Operon System with oveview of lac operon, trp operon, ara operon","isSavedByCurrentUser":false,"pageCount":38,"score":0,"slideshowId":"273481808","sourceName":"LATEST","strippedTitle":"operon-system-with-oveview-of-lac-operon-trp-operon-ara-operon","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/operonsystem-241121013930-40337d4e-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"An operon is a functional unit of DNA in prokaryotes that contains a group of genes regulated together.\r\nThese genes are transcribed as a single mRNA molecule, allowing coordinated expression of proteins involved in related functions (e.g., metabolic pathways).\r\n","tags":["operon system (lac","trp","ara)"],"url":"https://www.slideshare.net/slideshow/operon-system-with-oveview-of-lac-operon-trp-operon-ara-operon/273481808","userLogin":"DeepanshuBanyal","userName":"DeepanshuBanyal","viewCount":79},{"algorithmId":"4","displayTitle":"Cell division \u0026 Cell Cycle (Prepared by Taslima Khatun)","isSavedByCurrentUser":false,"pageCount":19,"score":0,"slideshowId":"273467629","sourceName":"LATEST","strippedTitle":"cell-division-cell-cycle-prepared-by-taslima-khatun","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/celldivisioncellcycle-241120125925-6232d2ff-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"Cell division and Cell Cycle.","tags":["#cell division #cell cycle"],"url":"https://www.slideshare.net/slideshow/cell-division-cell-cycle-prepared-by-taslima-khatun/273467629","userLogin":"taslimkhatuntaslima4","userName":"taslimkhatuntaslima4","viewCount":73},{"algorithmId":"4","displayTitle":"Amino Acid Metabolism - Synthesis \u0026 Breakdown.pptx","isSavedByCurrentUser":false,"pageCount":15,"score":0,"slideshowId":"273434735","sourceName":"LATEST","strippedTitle":"amino-acid-metabolism-synthesis-breakdown-pptx","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/aminoacidmetabolism-241119131517-33d70c97-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"Amino acid metabolism involves synthesis, degradation, and conversion to energy, glucose, or other molecules.","tags":["#aminoacidmetabolism","#proteinbiochemistry","#transamination"],"url":"https://www.slideshare.net/slideshow/amino-acid-metabolism-synthesis-breakdown-pptx/273434735","userLogin":"rituparnas","userName":"rituparnas","viewCount":86},{"algorithmId":"4","displayTitle":"Ribozymes : Biochemistry of Ribozymes and Catalytic Antibodies","isSavedByCurrentUser":false,"pageCount":23,"score":0,"slideshowId":"273522777","sourceName":"LATEST","strippedTitle":"ribozymes-biochemistry-of-ribozymes-and-catalytic-antibodies","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/ribozymesandabzymes-241122105123-31265dd8-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"Ribozymes are enzymes whose catalytic centers are composed entirely of RNA and therefore do not require proteins for catalysis (although many exist naturally as RNA–protein complexes).\n\nAntibodies are glycoproteins, termed immunoglobins (Igs), that bind with high specificity to antigens. An antibody with catalytic antibody is called abzymes (catalytic antibody).\n","tags":["biology","biotechnology","biomedical"],"url":"https://www.slideshare.net/slideshow/ribozymes-biochemistry-of-ribozymes-and-catalytic-antibodies/273522777","userLogin":"DeepanshuBanyal","userName":"DeepanshuBanyal","viewCount":40},{"algorithmId":"4","displayTitle":"Newton's Laws of Motion-Force and Motion at work (2).pptx","isSavedByCurrentUser":false,"pageCount":13,"score":0,"slideshowId":"273557026","sourceName":"LATEST","strippedTitle":"newton-s-laws-of-motion-force-and-motion-at-work-2-pptx","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/soundisproducedbyvibrations-241124090221-7ec3401d-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"Learning Objectives:\n To know how is Force and Motion related?\nTo know what did Isaac Newton observe?\nTo know what are Newton's Three Laws of Motion and its examples?\n______________________\nRECAP\nNewton's First Law of Motion-The Law of Inertia\nNewton's Second Law of Motion-The Law of Acceleration\nNewton's Third Law of Motion-The Law of Action and Reaction\n\n","tags":[],"url":"https://www.slideshare.net/slideshow/newton-s-laws-of-motion-force-and-motion-at-work-2-pptx/273557026","userLogin":"arshaaji2022","userName":"arshaaji2022","viewCount":15},{"algorithmId":"4","displayTitle":"(CBSE class 12 project on) study of various factors on which the internal res...","isSavedByCurrentUser":false,"pageCount":13,"score":0,"slideshowId":"273549255","sourceName":"LATEST","strippedTitle":"cbse-class-12-project-on-study-of-various-factors-on-which-the-internal-resistance-emf-of-a-cell-depends","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/factorsonwhichinternalresistance-241123190243-e10c50a0-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"Factors on which the internal resistance/emf of a cell depends\n(CBSE class 12 project on) study of various factors on which the internal resistance/emf of a cell depends. \n\n. The document outlines an experiment to study the various factors that affect the internal resistance and electromotive force (emf) of a cell. 2. It includes an introduction on electrochemical cells, the objective of studying how internal resistance/emf depends on different factors, and the apparatus used. 3. The procedure involves measuring the internal resistance of a cell by varying the shunt resistance in a Wheatstone bridge circuit and measuring the balancing lengths, and studying how resistance changes with electrode separation, electrolyte temperature, and concentration\nThe internal resistance and electromotive force (emf) of a cell are influenced by several factors:\nDistance Between Electrodes: A larger separation increases the length of the electrolyte, leading to higher internal resistance due to greater opposition to ion flow13.\nNature and Concentration of the Electrolyte: The type and concentration affect conductivity; higher concentrations generally reduce internal resistance24.\nArea of Electrodes: Increased surface area in contact with the electrolyte lowers internal resistance, enhancing ion movement34.\nTemperature: Higher temperatures typically decrease internal resistance by increasing ion mobility within the electrolyte3.\nThese factors collectively determine the efficiency and performance of electrochemical cells.\nThe nature of the electrolyte significantly affects the internal resistance of a cell through several mechanisms:\nConductivity: Different electrolytes have varying levels of ionic conductivity. Electrolytes with higher conductivity allow ions to move more freely, reducing internal resistance. Conversely, electrolytes with lower conductivity increase resistance due to restricted ion flow 14.\nConcentration: The concentration of the electrolyte influences the number of ions available for conduction. Higher concentrations generally lead to more ions per unit volume, which can decrease internal resistance by facilitating better current flow 23.\nTemperature Dependence: As temperature increases, the kinetic energy of ions rises, enhancing their mobility and reducing internal resistance. However, this effect can vary based on the specific electrolyte used 24.\nOverall, the choice and concentration of the electrolyte are crucial for optimizing a cell's performance by minimizing internal resistance.\nCreated by Suryavansh Rana ","tags":["(cbse class 12 project on","factors on which the internal","internal resistance"],"url":"https://www.slideshare.net/slideshow/cbse-class-12-project-on-study-of-various-factors-on-which-the-internal-resistance-emf-of-a-cell-depends/273549255","userLogin":"tfluid16","userName":"tfluid16","viewCount":31},{"algorithmId":"4","displayTitle":"Functions of Law Enforcement Agencies.pptx","isSavedByCurrentUser":false,"pageCount":12,"score":0,"slideshowId":"273538755","sourceName":"LATEST","strippedTitle":"functions-of-law-enforcement-agencies-pptx","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/functionsoflawenforcementagencies-241123061451-12b4111e-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"Following presentation explains about the functions of law enforcement agencies and function of police officials in general","tags":[],"url":"https://www.slideshare.net/slideshow/functions-of-law-enforcement-agencies-pptx/273538755","userLogin":"nivyag2","userName":"Nivya George","viewCount":23},{"algorithmId":"4","displayTitle":"Solid waste - Characterisation and sorting","isSavedByCurrentUser":false,"pageCount":25,"score":0,"slideshowId":"273566286","sourceName":"LATEST","strippedTitle":"solid-waste-characterisation-and-sorting","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/solidwaste-241124215753-20dfd4ca-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"### Solid Waste: Characterization and Sorting\n\n#### 1. Introduction to Solid Waste\nSolid waste refers to a variety of discarded materials that are no longer useful, which can originate from residential, commercial, industrial, or agricultural sources. Understanding the composition and characteristics of solid waste is crucial for effective waste management strategies.\n\n#### 2. Characterization of Solid Waste\nCharacterization involves analyzing the types and quantities of waste produced. This can include:\n\n- **Physical Characteristics**:\n - **Size**: Particle size can affect disposal and processing options.\n - **Density**: This influences transportation and storage.\n - **Moisture Content**: High moisture can lead to increased weight and potential for decomposition.\n\n- **Chemical Characteristics**:\n - **Organic Compounds**: Presence of biodegradable materials like food waste.\n - **Inorganic Compounds**: Includes metals, plastics, and glass.\n - **Hazardous Materials**: Substances that pose a risk to health or the environment, such as batteries or chemicals.\n\n- **Biological Characteristics**:\n - **Decomposability**: Ability of waste to break down naturally.\n - **Microbial Activity**: Presence of bacteria and other microorganisms that can aid in decomposition.\n\n#### 3. Sorting of Solid Waste\nSorting is a critical step in waste management that helps in recycling and reducing landfill use. It can be performed at various stages:\n\n- **At the Source**:\n - **Household Sorting**: Encouraging residents to separate recyclables (plastics, paper, metals) from general waste.\n - **Commercial Practices**: Businesses can implement waste sorting systems to minimize waste.\n\n- **Centralized Sorting Facilities**:\n - **Material Recovery Facilities (MRFs)**: These facilities sort mixed waste into different categories for recycling and recovery.\n\n- **Types of Sorting**:\n - **Manual Sorting**: Workers physically separate materials.\n - **Mechanical Sorting**: Use of machines like conveyor belts, shredders, and air classifiers to automate the process.\n \n#### 4. Benefits of Characterization and Sorting\n- **Enhanced Recycling**: Improved sorting increases the quality and quantity of recyclable materials.\n- **Reduced Waste Volume**: Effective sorting can significantly reduce the amount of waste sent to landfills.\n- **Environmental Protection**: Minimizes the impact of hazardous waste on ecosystems.\n- **Resource Recovery**: Allows for the recovery of valuable materials, contributing to a circular economy.\n\n#### 5. Conclusion\nCharacterizing and sorting solid waste are essential for sustainable waste management. By understanding the types of waste generated and implementing effective sorting practices, communities can improve recycling rates, reduce environmental impacts, and promote resource conservation.","tags":["environmental biotechnology"],"url":"https://www.slideshare.net/slideshow/solid-waste-characterisation-and-sorting/273566286","userLogin":"nesmasamad11","userName":"nesmasamad11","viewCount":14},{"algorithmId":"4","displayTitle":"GENE THERAPY_By Vanshika Chauhan(Bsc. BIOMEDICAL SCIENCE)1ST YEAR","isSavedByCurrentUser":false,"pageCount":14,"score":0,"slideshowId":"273392619","sourceName":"LATEST","strippedTitle":"gene-therapy_by-vanshika-chauhan-bsc-biomedical-science-1st-year","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/genetherapy202410051922250000-241118061435-da149574-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"This presentation covers information about Gene Therapy. Gene therapy is a medical technique that involves modifying or replacing defective genes within a person's cells to treat or prevent diseases. It aims to address genetic disorders by:\n\n1. Replacing faulty genes with healthy ones.\n\n\n2. Inactivating harmful genes to prevent disease progression.\n\n\n3. Introducing new genes to help fight or cure diseases.\n\n\n\nGene therapy is primarily used for conditions like genetic disorders, cancer, and certain viral infections. It typically uses vectors like viruses to deliver genetic material into cells.\n\n","tags":[],"url":"https://www.slideshare.net/slideshow/gene-therapy_by-vanshika-chauhan-bsc-biomedical-science-1st-year/273392619","userLogin":"chauhanvanshika538","userName":"chauhanvanshika538","viewCount":38},{"algorithmId":"4","displayTitle":"Theoretical Concepts of Acids \u0026 Bases.pptx","isSavedByCurrentUser":false,"pageCount":13,"score":0,"slideshowId":"273435172","sourceName":"LATEST","strippedTitle":"theoretical-concepts-of-acids-bases-pptx","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/theoryofacidsbases-241119132649-56a3050e-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"Explains acids and bases using Arrhenius, Brønsted-Lowry, and Lewis' theories with real-world examples.","tags":["#biochemistry","#chemistrybasics","#"],"url":"https://www.slideshare.net/slideshow/theoretical-concepts-of-acids-bases-pptx/273435172","userLogin":"rituparnas","userName":"rituparnas","viewCount":64},{"algorithmId":"4","displayTitle":"【加拿大毕业证书定制】安大略艺术设计学院OCAD毕业证学位证留学生如何办理","isSavedByCurrentUser":false,"pageCount":13,"score":0,"slideshowId":"273428119","sourceName":"LATEST","strippedTitle":"pc5yv2-pptx","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/random-241119093552-1b751f4c-thumbnail.jpg?width=600\u0026height=600\u0026fit=bounds","description":"加拿大毕业证书定制-原版高精度还原【办证威信-QQ/: 74100 3700】安大略艺术设计学院OCAD毕业证和学位证书、成绩单、offer留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。\r\n\r\n主要有文凭办理、毕业证购买、毕业证成绩单购买、文凭购买，毕业证办理业务。一比一还原国外大学毕业证，定制国外大学学历，制作国外大学文凭，复刻国外大学毕业证书。办理毕业证、办理文凭、买大学毕业证、买大学文凭，海量学校供您选择 · 学校真实原版工艺 · 十年从业经验专业可靠 · 您值得信赖的合作商家！\r\n\r\n【关于学历材料质量】\r\n我们承诺采用的是学校原版纸张（原版纸质、底色、纹路）我们工厂拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有成品以及工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！提供美国毕业证制作、加拿大毕业证定做、美国文凭订购、加拿大大学毕业证、美国大学文凭办理，加拿大文凭办理等业务。\r\n【业务选择办理准则】\r\n一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号bwp0011】文凭即可\r\n二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号bwp0011】即可\r\n三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。\r\n\r\n【成绩单有没有必要办理】\r\n成绩单的意义主要体现在证明学习能力、评估学术背景、展示综合素质、提高录取率，以及是作为留信认证申请材料的一部分。\r\n成绩单能够体现您的的学习能力，包括课程成绩、专业能力、研究能力。具体来说，成绩报告单通常包含学生的学习技能与习惯、各科成绩以及老师评语等部分，因此，成绩单不仅是学生学术能力的证明，也是评估学生是否适合某个教育项目的重要依据！\r\n\r\n留信网认证的作用:\r\n1:该专业认证可证明留学生真实身份\r\n2:同时对留学生所学专业登记给予评定\r\n3:国家专业人才认证中心颁发入库证书\r\n4:这个认证书并且可以归档倒地方\r\n5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息\r\n6:个人职称评审加20分\r\n7:个人信誉贷款加10分\r\n8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才\r\n\r\n留信网服务项目：\r\n1、留学生专业人才库服务（留信分析）\r\n2、国（境）学习人员提供就业推荐信服务\r\n3、留学人员区块链存储服务\r\n\r\n国外留学回国的学生都清楚学历认证【微信:bwp0011】的重要性，一些在留学期间因为意外情况被开除不能毕业的学生，即便在无学位的情况下，也想费尽心思申请一份学历认证为自己证明国外的学位情况。\r\n留学认证学历从最简单的层面来说是对你纪念学习生涯画上一个完美的句号,从另一种角度来说也是对自己的一种交代，更是一种学习能力的证明!\r\n\r\n留信网和中留服的区别：【微信：bwp0011】\r\n办理安大略艺术设计学院OCAD毕业证【微信:bwp0011】offer/学位证、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作\r\n留信网的主办单位是北京留信信息科学研究院，主要职责就是为留学归国人员提供留学生就业等人力资源服务，提供“境外校库”海外院校办学信息查询。留信认证主要是出具“留学生专业人才入库证明”，以及一个留信网网络查询留学经历数据分析报告。\r\n\r\n留服即中国留学服务中心，是教育部直属事业单位，主要从事出国留学、留学回国、来华留学以及教育国际交流与合作等领域的相关服务，其中国企，考公，落户，升学等都是需要留服认证的。\r\n\r\n两种认证用处有所差异，大家肯定都想做更有用的留服认证。须知，留服认证只有在正规大学或项目就读，顺利毕业取得学位的情况下才能认证通过，如果是留学未能完成学业的，没有取得毕业相关证书，则无法通过认证。\r\n\r\n在这种情况下，国外留学无法毕业的留学生如果想要直接认证，则只能选择留信认证了。这种方式可以给予因为各种原因在国外无法完成学业，被退学，被开除的同学更多选择的可能，更多证明留学经历学习背景的机会。\r\n办理安大略艺术设计学院OCAD毕业证【微信:bwp0011】offer/学位证、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作。\r\n\r\n办理安大略艺术设计学院OCAD毕业证/学位证【qq:74100 3700】在读证明信/文凭证书、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作\r\n定制安大略艺术设计学院OCAD毕业证成绩单【qq:74100 3700】不同学院专业模版基本一致，不同年份版本有所区别，严格按照不同年份版本来定制。\r\n留学：一场跨越国界的成长之旅\r\n在人生的广阔画卷中，留学无疑是最为绚烂多彩的一笔。它不仅仅是一次地理上的迁徙，更是心灵与智慧的深度游历，是自我挑战与重塑的宝贵机遇。当飞机划过天际，远离熟悉的土地，每一位踏上留学征途的学子，都怀揣着梦想与不安，迈向了一个全新的世界。\r\n文化的碰撞与融合\r\n留学，首先是一场文化的盛宴。走进异国他乡，每一处风景、每一道菜肴、每一种语言，都是文化的独特印记\r\n总之，留学是一场充满挑战与机遇的旅程。它让学子们在文化的碰撞中拓宽视野，在学术的深耕中提升自我，在独立与成长的磨砺中变得坚韧不拔，在人际关系的构建中拓展世界。这段经历，将成为他们人生中最宝贵的财富之一，激励他们在未来的道路上勇往直前，不断探索未知的世界","tags":["安大略艺术设计学院ocad毕业证"],"url":"https://www.slideshare.net/slideshow/pc5yv2-pptx/273428119","userLogin":"2uewfxnc","userName":"2uewfxnc","viewCount":22}]},"secretUrl":"HfRD6lclORlqfS","shouldShowAds":true,"slides":{"host":"https://image.slidesharecdn.com","title":"Understanding-Feature-Space-in-Machine-Learning","imageLocation":"understanding-feature-space-150910214435-lva1-app6892","imageSizes":[{"quality":85,"width":320,"format":"jpg"},{"quality":85,"width":638,"format":"jpg"},{"quality":75,"width":2048,"format":"webp"}]},"smsShareUrl":"sms:?body=Check out this SlideShare : https://www.slideshare.net/slideshow/understanding-feature-space-in-machine-learning/52649207","strippedTitle":"understanding-feature-space-in-machine-learning","thumbnail":"https://cdn.slidesharecdn.com/ss_thumbnails/understanding-feature-space-150910214435-lva1-app6892-thumbnail.jpg?width=640\u0026height=640\u0026fit=bounds","title":"Understanding Feature Space in Machine Learning","totalSlides":33,"transcript":["Understanding\nFeature Space in\nMachine Learning\nAlice Zheng, Dato\nSeptember 9, 2015\n1\n ","2\nMy journey so far\nApplied machine learning\n(Data science)\nBuild ML tools\nShortage of experts\nand good tools.\n ","3\nWhy machine learning?\nModel data.\nMake predictions.\nBuild intelligent\napplications.\n ","4\nThe machine learning pipeline\nI fell in love the instant I laid\nmy eyes on that puppy. His\nbig eyes and playful tail, his\nsoft furry paws, …\nRaw data\nFeatures\nModels\nPredictions\nDeploy in\nproduction\n ","Feature = numeric representation of raw data\n ","6\nRepresenting natural text\nIt is a puppy and it is\nextremely cute.\nWhat’s important?\nPhrases? Specific\nwords? Ordering?\nSubject, object, verb?\nClassify:\npuppy or not?\nRaw Text\n{“it”:2,\n“is”:2,\n“a”:1,\n“puppy”:1,\n“and”:1,\n“extremely”:1,\n“cute”:1 }\nBag of Words\n ","7\nRepresenting natural text\nIt is a puppy and it is\nextremely cute.\nClassify:\npuppy or not?\nRaw Text Bag of Words\nit 2\nthey 0\nI 1\nam 0\nhow 0\npuppy 1\nand 1\ncat 0\naardvark 0\ncute 1\nextremely 1\n… …\nSparse vector\nrepresentation\n ","8\nRepresenting images\nImage source: “Recognizing and learning object categories,”\nLi Fei-Fei, Rob Fergus, Anthony Torralba, ICCV 2005—2009.\nRaw image:\nmillions of RGB triplets,\none for each pixel\nClassify:\nperson or animal?\nRaw Image Bag of Visual Words\n ","9\nRepresenting images\nClassify:\nperson or animal?\nRaw Image Deep learning features\n3.29\n-15\n-5.24\n48.3\n1.36\n47.1\n-\n1.92\n36.5\n2.83\n95.4\n-19\n-89\n5.09\n37.8\nDense vector\nrepresentation\n ","10\nFeature space in machine learning\n• Raw data  high dimensional vectors\n• Collection of data points  point cloud in feature space\n• Model = geometric summary of point cloud\n• Feature engineering = creating features of the appropriate\ngranularity for the task\n ","Crudely speaking, mathematicians fall into two\ncategories: the algebraists, who find it easiest to\nreduce all problems to sets of numbers and\nvariables, and the geometers, who understand the\nworld through shapes.\n-- Masha Gessen, “Perfect Rigor”\n ","12\nAlgebra vs. Geometry\na\nb\nc\na2 + b2 = c2\nAlgebra Geometry\nPythagorean\nTheorem\n(Euclidean space)\n ","13\nVisualizing a sphere in 2D\nx2 + y2 = 1\na\nb\nc\nPythagorean theorem:\na2 + b2 = c2\nx\ny\n1\n1\n ","14\nVisualizing a sphere in 3D\nx2 + y2 + z2 = 1\nx\ny\nz\n1\n1\n1\n ","15\nVisualizing a sphere in 4D\nx2 + y2 + z2 + t2 = 1\nx\ny\nz\n1\n1\n1\n ","16\nWhy are we looking at spheres?\n= =\n= =\nPoincaré Conjecture:\nAll physical objects without holes\nis “equivalent” to a sphere.\n ","17\nThe power of higher dimensions\n• A sphere in 4D can model the birth and death process of\nphysical objects\n• Point clouds = approximate geometric shapes\n• High dimensional features can model many things\n ","Visualizing Feature Space\n ","19\nThe challenge of high dimension geometry\n• Feature space can have hundreds to millions of\ndimensions\n• In high dimensions, our geometric imagination is limited\n- Algebra comes to our aid\n ","20\nVisualizing bag-of-words\npuppy\ncute\n1\n1\nI have a puppy and\nit is extremely cute\nI have a puppy and\nit is extremely cute\nit 1\nthey 0\nI 1\nam 0\nhow 0\npuppy 1\nand 1\ncat 0\naardvark 0\nzebra 0\ncute 1\nextremely 1\n… …\n ","21\nVisualizing bag-of-words\npuppy\ncute\n1\n1\n1\nextremely\nI have a puppy and\nit is extremely cute\nI have an extremely\ncute cat\nI have a cute\npuppy\n ","22\nDocument point cloud\nword 1\nword 2\n ","23\nWhat is a model?\n• Model = mathematical “summary” of data\n• What’s a summary?\n- A geometric shape\n ","24\nClassification model\nFeature 2\nFeature 1\nDecide between two classes\n ","25\nClustering model\nFeature 2\nFeature 1\nGroup data points tightly\n ","26\nRegression model\nTarget\nFeature\nFit the target values\n ","Visualizing Feature Engineering\n ","28\nWhen does bag-of-words fail?\npuppy\ncat\n2\n1\n1\nhave\nI have a puppy\nI have a cat\nI have a kitten\nTask: find a surface that separates\ndocuments about dogs vs. cats\nProblem: the word “have” adds fluff\ninstead of information\nI have a dog\nand I have a pen\n1\n ","29\nImproving on bag-of-words\n• Idea: “normalize” word counts so that popular words\nare discounted\n• Term frequency (tf) = Number of times a terms\nappears in a document\n• Inverse document frequency of word (idf) =\n• N = total number of documents\n• Tf-idf count = tf x idf\n ","30\nFrom BOW to tf-idf\npuppy\ncat\n2\n1\n1\nhave\nI have a puppy\nI have a cat\nI have a kitten\nidf(puppy) = log 4\nidf(cat) = log 4\nidf(have) = log 1 = 0\nI have a dog\nand I have a pen\n1\n ","31\nFrom BOW to tf-idf\npuppy\ncat1\nhave\ntfidf(puppy) = log 4\ntfidf(cat) = log 4\ntfidf(have) = 0\nI have a dog\nand I have a pen,\nI have a kitten\n1\nlog 4\nlog 4\nI have a cat\nI have a puppy\nDecision surface\nTf-idf flattens\nuninformative\ndimensions in the\nBOW point cloud\n ","32\nEntry points of feature engineering\n• Start from data and task\n- What’s the best text representation for classification?\n• Start from modeling method\n- What kind of features does k-means assume?\n- What does linear regression assume about the data?\n ","33\nThat’s not all, folks!\n• There’s a lot more to feature engineering:\n- Feature normalization\n- Feature transformations\n- “Regularizing” models\n- Learning the right features\n• Dato is hiring! jobs@dato.com\nalicez@dato.com @RainyData\n "],"twitterShareUrl":"https://twitter.com/intent/tweet?via=SlideShare\u0026text=Understanding+Feature+Space+in+Machine+Learning+by+%40RainyData+%23featurespace+%23features+https%3A%2F%2Fwww.slideshare.net%2Fslideshow%2Funderstanding-feature-space-in-machine-learning%2F52649207","type":"presentation","slideDimensions":{"height":540,"width":960},"topReadSlides":[{"slideIndex":25,"ranking":1},{"slideIndex":23,"ranking":2},{"slideIndex":21,"ranking":3}],"user":{"id":"82459696","isFollowing":false,"login":"AliceZheng3","name":"Alice Zheng","occupation":"Sr Manager, Applied Science at Amazon - Hiring research software engineers and managers","organization":"Amazon","photo":"https://cdn.slidesharecdn.com/profile-photo-AliceZheng3-48x48.jpg?cb=1530851967","photoExists":true,"shortName":"Alice Zheng"},"views":30126},"_nextI18Next":{"initialI18nStore":{"en":{"common":{"ad":{"fallbackText":"Ad for Scribd subscription","label":"Ad","close":"Close Ad","dismiss_in":"Dismiss in","ad_info_title":"Why are you seeing this?","ad_info_description":"We use ads to keep content free and accessible for everyone. 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