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dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConvolutional Neural Networks Project Notes\u003c/h1\u003e\u003ca id=\"user-content-convolutional-neural-networks-project-notes\" class=\"anchor\" aria-label=\"Permalink: Convolutional Neural Networks Project Notes\" href=\"#convolutional-neural-networks-project-notes\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#convolutional-neural-networks\"\u003eConvolutional Neural Networks\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#review-of-the-whole-project-and-course\"\u003eReview of the whole Project and Course\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#convolutional-neural-networks-step-by-step\"\u003eConvolutional Neural Networks: Step by Step\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#convolutional-neural-networks-application\"\u003eConvolutional Neural Networks: Application\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#keras-tutorial---the-happy-house\"\u003eKeras tutorial - the Happy House\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#residual-networks\"\u003eResidual Networks\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#deep-learning--art-neural-style-transfer\"\u003eArt generation with Neural Style Transfer\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#autonomous-driving---car-detection\"\u003eAutonomous driving - Car detection\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eThis repository is the summaries of the project Convolutional Neural Networks on \u003ca href=\"https://deeplearning.ai\" rel=\"nofollow\"\u003eDeepLearning.ai\u003c/a\u003e specialization courses.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConvolutional Neural Networks\u003c/h2\u003e\u003ca id=\"user-content-convolutional-neural-networks\" class=\"anchor\" aria-label=\"Permalink: Convolutional Neural Networks\" href=\"#convolutional-neural-networks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eReview of the whole Project and Course\u003c/h3\u003e\u003ca id=\"user-content-review-of-the-whole-project-and-course\" class=\"anchor\" aria-label=\"Permalink: Review of the whole Project and Course\" href=\"#review-of-the-whole-project-and-course\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eThis course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou will:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eUnderstand how to build a convolutional neural network, including recent variations such as residual networks.\u003c/li\u003e\n\u003cli\u003eKnow how to apply convolutional networks to visual detection and recognition tasks.\u003c/li\u003e\n\u003cli\u003eKnow to use neural style transfer to generate art.\u003c/li\u003e\n\u003cli\u003eBe able to apply these algorithms to a variety of image, video, and other 2D or 3D data.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConvolutional Neural Networks: Step by Step\u003c/h3\u003e\u003ca id=\"user-content-convolutional-neural-networks-step-by-step\" class=\"anchor\" aria-label=\"Permalink: Convolutional Neural Networks: Step by Step\" href=\"#convolutional-neural-networks-step-by-step\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWelcome to Course 4's first assignment! In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagation.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNotation\u003c/strong\u003e:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eSuperscript \u003csup\u003e[l]\u003c/sup\u003e denotes an object of the \u003ccode\u003el.th\u003c/code\u003e layer.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eExample: a\u003csup\u003e[4]\u003c/sup\u003e is the \u003ccode\u003e4.th\u003c/code\u003e layer activation. W\u003csup\u003e[5]\u003c/sup\u003e and b\u003csup\u003e[5]\u003c/sup\u003e are the \u003ccode\u003e5.\u0026lt;sup\u0026gt;th\u0026lt;/sup\u0026gt;\u003c/code\u003e layer parameters.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eSuperscript \u003csup\u003e(i)\u003c/sup\u003e denotes an object from the \u003ccode\u003ei.th\u003c/code\u003e example.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eExample: x\u003csup\u003e(i)\u003c/sup\u003e is the \u003ccode\u003ei.th\u003c/code\u003e training example input.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eLowerscript \u003csub\u003ei\u003c/sub\u003e denotes the \u003ccode\u003ei.th\u003c/code\u003e entry of a vector.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eExample: a\u003csup\u003e[l]\u003c/sup\u003e\u003csub\u003ei\u003c/sub\u003e denotes the \u003ccode\u003ei.th\u003c/code\u003e entry of the activations in layer \u003ccode\u003el\u003c/code\u003e, assuming this is a fully connected (FC) layer.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003en_H\u003c/code\u003e, \u003ccode\u003en_W\u003c/code\u003e and \u003ccode\u003en_C\u003c/code\u003e denote respectively the height, width and number of channels of a given layer. If you want to reference a specific layer \u003ccode\u003el\u003c/code\u003e, you can also write n_H\u003csup\u003e[l]\u003c/sup\u003e, n_W\u003csup\u003e[l]\u003c/sup\u003e, n_C\u003csup\u003e[l]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003en_H_prev\u003c/code\u003e, \u003ccode\u003en_W_prev\u003c/code\u003e and \u003ccode\u003en_C_prev\u003c/code\u003e denote respectively the height, width and number of channels of the previous layer. If referencing a specific layer \u003ccode\u003el\u003c/code\u003e, this could also be denoted n_H\u003csup\u003e[l-1]\u003c/sup\u003e, n_W\u003csup\u003e[l-1]\u003c/sup\u003e, n_C\u003csup\u003e[l-1]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eWe assume that you are already familiar with \u003ccode\u003enumpy\u003c/code\u003e and/or have completed the previous courses of the specialization. Let's get started!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConvolutional Neural Networks: Application\u003c/h3\u003e\u003ca id=\"user-content-convolutional-neural-networks-application\" class=\"anchor\" aria-label=\"Permalink: Convolutional Neural Networks: Application\" href=\"#convolutional-neural-networks-application\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWelcome to Course 4's second assignment! In this notebook, you will:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eImplement helper functions that you will use when implementing a TensorFlow model\u003c/li\u003e\n\u003cli\u003eImplement a fully functioning ConvNet using TensorFlow\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAfter this assignment you will be able to:\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBuild and train a ConvNet in TensorFlow for a classification problem\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eWe assume here that you are already familiar with TensorFlow. If you are not, please refer the \u003cem\u003eTensorFlow Tutorial\u003c/em\u003e of the third week of Course 2 (\"\u003cem\u003eImproving deep neural networks\u003c/em\u003e\").\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eKeras tutorial - the Happy House\u003c/h3\u003e\u003ca id=\"user-content-keras-tutorial---the-happy-house\" class=\"anchor\" aria-label=\"Permalink: Keras tutorial - the Happy House\" href=\"#keras-tutorial---the-happy-house\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWelcome to the first assignment of week 2. In this assignment, you will:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eLearn to use Keras, a high-level neural networks API (programming framework), written in Python and capable of running on top of several lower-level frameworks including TensorFlow and CNTK.\u003c/li\u003e\n\u003cli\u003eSee how you can in a couple of hours build a deep learning algorithm.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eWhy are we using Keras? Keras was developed to enable deep learning engineers to build and experiment with different models very quickly. Just as TensorFlow is a higher-level framework than Python, Keras is an even higher-level framework and provides additional abstractions. Being able to go from idea to result with the least possible delay is key to finding good models. However, Keras is more restrictive than the lower-level frameworks, so there are some very complex models that you can implement in TensorFlow but not (without more difficulty) in Keras. That being said, Keras will work fine for many common models.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn this exercise, you'll work on the \"Happy House\" problem, which we'll explain below. Let's load the required packages and solve the problem of the Happy House!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eResidual Networks\u003c/h3\u003e\u003ca id=\"user-content-residual-networks\" class=\"anchor\" aria-label=\"Permalink: Residual Networks\" href=\"#residual-networks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWelcome to the second assignment of this week! You will learn how to build very deep convolutional networks, using Residual Networks (ResNets). In theory, very deep networks can represent very complex functions; but in practice, they are hard to train. Residual Networks, introduced by \u003ca href=\"https://arxiv.org/pdf/1512.03385.pdf\" rel=\"nofollow\"\u003eHe et al.\u003c/a\u003e, allow you to train much deeper networks than were previously practically feasible.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eIn this assignment, you will:\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eImplement the basic building blocks of ResNets.\u003c/li\u003e\n\u003cli\u003ePut together these building blocks to implement and train a state-of-the-art neural network for image classification.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eThis assignment will be done in Keras.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eBefore jumping into the problem, let's run the cell below to load the required packages.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeep Learning \u0026amp; Art: Neural Style Transfer\u003c/h3\u003e\u003ca id=\"user-content-deep-learning--art-neural-style-transfer\" class=\"anchor\" aria-label=\"Permalink: Deep Learning \u0026amp; Art: Neural Style Transfer\" href=\"#deep-learning--art-neural-style-transfer\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWelcome to the second assignment of this week. In this assignment, you will learn about Neural Style Transfer. This algorithm was created by Gatys et al. (2015) (\u003ca href=\"https://arxiv.org/abs/1508.06576\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/1508.06576\u003c/a\u003e).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eIn this assignment, you will:\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eImplement the neural style transfer algorithm\u003c/li\u003e\n\u003cli\u003eGenerate novel artistic images using your algorithm\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eMost of the algorithms you've studied optimize a cost function to get a set of parameter values. In Neural Style Transfer, you'll optimize a cost function to get pixel values!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAutonomous driving - Car detection\u003c/h3\u003e\u003ca id=\"user-content-autonomous-driving---car-detection\" class=\"anchor\" aria-label=\"Permalink: Autonomous driving - Car detection\" href=\"#autonomous-driving---car-detection\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWelcome to your week 3 programming assignment. You will learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers: Redmon et al., 2016 (\u003ca href=\"https://arxiv.org/abs/1506.02640\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/1506.02640\u003c/a\u003e) and Redmon and Farhadi, 2016 (\u003ca href=\"https://arxiv.org/abs/1612.08242\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/1612.08242\u003c/a\u003e).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eYou will learn to\u003c/strong\u003e:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eUse object detection on a car detection dataset\u003c/li\u003e\n\u003cli\u003eDeal with bounding boxes\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eRun the following cell to load the packages and dependencies that are going to be useful for your journey!\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Convolutional Neural Networks Project Notes","anchor":"convolutional-neural-networks-project-notes","htmlText":"Convolutional Neural Networks Project Notes"},{"level":2,"text":"Convolutional Neural Networks","anchor":"convolutional-neural-networks","htmlText":"Convolutional Neural Networks"},{"level":3,"text":"Review of the whole Project and Course","anchor":"review-of-the-whole-project-and-course","htmlText":"Review of the whole Project and Course"},{"level":3,"text":"Convolutional Neural Networks: Step by Step","anchor":"convolutional-neural-networks-step-by-step","htmlText":"Convolutional Neural Networks: Step by Step"},{"level":3,"text":"Convolutional Neural Networks: Application","anchor":"convolutional-neural-networks-application","htmlText":"Convolutional Neural Networks: Application"},{"level":3,"text":"Keras tutorial - the Happy House","anchor":"keras-tutorial---the-happy-house","htmlText":"Keras tutorial - the Happy House"},{"level":3,"text":"Residual Networks","anchor":"residual-networks","htmlText":"Residual Networks"},{"level":3,"text":"Deep Learning \u0026 Art: Neural Style Transfer","anchor":"deep-learning--art-neural-style-transfer","htmlText":"Deep Learning \u0026amp; Art: Neural Style Transfer"},{"level":3,"text":"Autonomous driving - Car detection","anchor":"autonomous-driving---car-detection","htmlText":"Autonomous driving - Car detection"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FSiHaoShen%2FConvolutional-Neural-Networks"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"blob_edit_unsaved_changes_storage":true,"accessible_code_button":true}}}}</script> <div data-target="react-partial.reactRoot"><style data-styled="true" data-styled-version="5.3.11">.iVEunk{margin-top:16px;margin-bottom:16px;}/*!sc*/ 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0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Convolutional Neural Networks Project Notes</h1><a id="user-content-convolutional-neural-networks-project-notes" class="anchor" aria-label="Permalink: Convolutional Neural Networks Project Notes" href="#convolutional-neural-networks-project-notes"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="#convolutional-neural-networks">Convolutional Neural Networks</a> <ul dir="auto"> <li><a href="#review-of-the-whole-project-and-course">Review of the whole Project and Course</a></li> <li><a href="#convolutional-neural-networks-step-by-step">Convolutional Neural Networks: Step by Step</a></li> <li><a href="#convolutional-neural-networks-application">Convolutional Neural Networks: Application</a></li> <li><a href="#keras-tutorial---the-happy-house">Keras tutorial - the Happy House</a></li> <li><a href="#residual-networks">Residual Networks</a></li> <li><a href="#deep-learning--art-neural-style-transfer">Art generation with Neural Style Transfer</a></li> <li><a href="#autonomous-driving---car-detection">Autonomous driving - Car detection</a></li> </ul> </li> </ul> <p dir="auto">This repository is the summaries of the project Convolutional Neural Networks on <a href="https://deeplearning.ai" rel="nofollow">DeepLearning.ai</a> specialization courses.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Convolutional Neural Networks</h2><a id="user-content-convolutional-neural-networks" class="anchor" aria-label="Permalink: Convolutional Neural Networks" href="#convolutional-neural-networks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Review of the whole Project and Course</h3><a id="user-content-review-of-the-whole-project-and-course" class="anchor" aria-label="Permalink: Review of the whole Project and Course" href="#review-of-the-whole-project-and-course"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <blockquote> <p dir="auto">This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.</p> <p dir="auto">You will:</p> <ul dir="auto"> <li>Understand how to build a convolutional neural network, including recent variations such as residual networks.</li> <li>Know how to apply convolutional networks to visual detection and recognition tasks.</li> <li>Know to use neural style transfer to generate art.</li> <li>Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.</li> </ul> </blockquote> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Convolutional Neural Networks: Step by Step</h3><a id="user-content-convolutional-neural-networks-step-by-step" class="anchor" aria-label="Permalink: Convolutional Neural Networks: Step by Step" href="#convolutional-neural-networks-step-by-step"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Welcome to Course 4's first assignment! In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagation.</p> <p dir="auto"><strong>Notation</strong>:</p> <ul dir="auto"> <li> <p dir="auto">Superscript <sup>[l]</sup> denotes an object of the <code>l.th</code> layer.</p> <ul dir="auto"> <li>Example: a<sup>[4]</sup> is the <code>4.th</code> layer activation. W<sup>[5]</sup> and b<sup>[5]</sup> are the <code>5.<sup>th</sup></code> layer parameters.</li> </ul> </li> <li> <p dir="auto">Superscript <sup>(i)</sup> denotes an object from the <code>i.th</code> example.</p> <ul dir="auto"> <li>Example: x<sup>(i)</sup> is the <code>i.th</code> training example input.</li> </ul> </li> <li> <p dir="auto">Lowerscript <sub>i</sub> denotes the <code>i.th</code> entry of a vector.</p> <ul dir="auto"> <li>Example: a<sup>[l]</sup><sub>i</sub> denotes the <code>i.th</code> entry of the activations in layer <code>l</code>, assuming this is a fully connected (FC) layer.</li> </ul> </li> <li> <p dir="auto"><code>n_H</code>, <code>n_W</code> and <code>n_C</code> denote respectively the height, width and number of channels of a given layer. If you want to reference a specific layer <code>l</code>, you can also write n_H<sup>[l]</sup>, n_W<sup>[l]</sup>, n_C<sup>[l]</sup>.</p> </li> <li> <p dir="auto"><code>n_H_prev</code>, <code>n_W_prev</code> and <code>n_C_prev</code> denote respectively the height, width and number of channels of the previous layer. If referencing a specific layer <code>l</code>, this could also be denoted n_H<sup>[l-1]</sup>, n_W<sup>[l-1]</sup>, n_C<sup>[l-1]</sup>.</p> </li> </ul> <p dir="auto">We assume that you are already familiar with <code>numpy</code> and/or have completed the previous courses of the specialization. Let's get started!</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Convolutional Neural Networks: Application</h3><a id="user-content-convolutional-neural-networks-application" class="anchor" aria-label="Permalink: Convolutional Neural Networks: Application" href="#convolutional-neural-networks-application"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Welcome to Course 4's second assignment! In this notebook, you will:</p> <ul dir="auto"> <li>Implement helper functions that you will use when implementing a TensorFlow model</li> <li>Implement a fully functioning ConvNet using TensorFlow</li> </ul> <p dir="auto"><strong>After this assignment you will be able to:</strong></p> <ul dir="auto"> <li>Build and train a ConvNet in TensorFlow for a classification problem</li> </ul> <p dir="auto">We assume here that you are already familiar with TensorFlow. If you are not, please refer the <em>TensorFlow Tutorial</em> of the third week of Course 2 ("<em>Improving deep neural networks</em>").</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Keras tutorial - the Happy House</h3><a id="user-content-keras-tutorial---the-happy-house" class="anchor" aria-label="Permalink: Keras tutorial - the Happy House" href="#keras-tutorial---the-happy-house"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Welcome to the first assignment of week 2. In this assignment, you will:</p> <ol dir="auto"> <li>Learn to use Keras, a high-level neural networks API (programming framework), written in Python and capable of running on top of several lower-level frameworks including TensorFlow and CNTK.</li> <li>See how you can in a couple of hours build a deep learning algorithm.</li> </ol> <p dir="auto">Why are we using Keras? Keras was developed to enable deep learning engineers to build and experiment with different models very quickly. Just as TensorFlow is a higher-level framework than Python, Keras is an even higher-level framework and provides additional abstractions. Being able to go from idea to result with the least possible delay is key to finding good models. However, Keras is more restrictive than the lower-level frameworks, so there are some very complex models that you can implement in TensorFlow but not (without more difficulty) in Keras. That being said, Keras will work fine for many common models.</p> <p dir="auto">In this exercise, you'll work on the "Happy House" problem, which we'll explain below. Let's load the required packages and solve the problem of the Happy House!</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Residual Networks</h3><a id="user-content-residual-networks" class="anchor" aria-label="Permalink: Residual Networks" href="#residual-networks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Welcome to the second assignment of this week! You will learn how to build very deep convolutional networks, using Residual Networks (ResNets). In theory, very deep networks can represent very complex functions; but in practice, they are hard to train. Residual Networks, introduced by <a href="https://arxiv.org/pdf/1512.03385.pdf" rel="nofollow">He et al.</a>, allow you to train much deeper networks than were previously practically feasible.</p> <p dir="auto"><strong>In this assignment, you will:</strong></p> <ul dir="auto"> <li>Implement the basic building blocks of ResNets.</li> <li>Put together these building blocks to implement and train a state-of-the-art neural network for image classification.</li> </ul> <p dir="auto">This assignment will be done in Keras.</p> <p dir="auto">Before jumping into the problem, let's run the cell below to load the required packages.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Deep Learning & Art: Neural Style Transfer</h3><a id="user-content-deep-learning--art-neural-style-transfer" class="anchor" aria-label="Permalink: Deep Learning & Art: Neural Style Transfer" href="#deep-learning--art-neural-style-transfer"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Welcome to the second assignment of this week. In this assignment, you will learn about Neural Style Transfer. This algorithm was created by Gatys et al. (2015) (<a href="https://arxiv.org/abs/1508.06576" rel="nofollow">https://arxiv.org/abs/1508.06576</a>).</p> <p dir="auto"><strong>In this assignment, you will:</strong></p> <ul dir="auto"> <li>Implement the neural style transfer algorithm</li> <li>Generate novel artistic images using your algorithm</li> </ul> <p dir="auto">Most of the algorithms you've studied optimize a cost function to get a set of parameter values. In Neural Style Transfer, you'll optimize a cost function to get pixel values!</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Autonomous driving - Car detection</h3><a id="user-content-autonomous-driving---car-detection" class="anchor" aria-label="Permalink: Autonomous driving - Car detection" href="#autonomous-driving---car-detection"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Welcome to your week 3 programming assignment. You will learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers: Redmon et al., 2016 (<a href="https://arxiv.org/abs/1506.02640" rel="nofollow">https://arxiv.org/abs/1506.02640</a>) and Redmon and Farhadi, 2016 (<a href="https://arxiv.org/abs/1612.08242" rel="nofollow">https://arxiv.org/abs/1612.08242</a>).</p> <p dir="auto"><strong>You will learn to</strong>:</p> <ul dir="auto"> <li>Use object detection on a car detection dataset</li> <li>Deal with bounding boxes</li> </ul> <p dir="auto">Run the following cell to load the packages and dependencies that are going to be useful for your journey!</p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="KPJu/+HIiLRHUIcIkK/5wOMvu291sSKxIC+4xDdD/EfuD0FgzVq4tfuwqYAmIXkR6y1ofR+SmNdtfBriGGnR6A==" /> </div> <div data-view-component="true" class="Layout-sidebar"> 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