Notebooks – colab.google

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top:10px; @media (max-width: 925px) { display:none; } } </style> <div style="right:255px" class="header_button"> <a class="button" href="https://colab.research.google.com">Open Colab</a> </div> <div style="right:110px" class="header_button"> <a class="button" href="https://colab.new">New Notebook</a> </div> <div style="right:20px" class="header_button"> <a class="button" href="https://colab.research.google.com/signup">Sign Up</a> </div> </header> <main class="section-wrapper" ng-controller="MainController as mainCtrl"> <section class="hero hero--detail has-mobile-bg" > <div class="hero__background " data-ng-lazy-background="/static/images/tools/tools_hero.jpg"> </div> <div class="hero__background--mobile" data-ng-lazy-background="/static/images/tools/tools_hero-m.jpg"></div> <div class="hero__wrapper"> <div class="hero__content-wrapper"> <div class="hero__content" style="padding: 50px;" <div class="content hero__content-inner " > <div class="content__text" > <span class="content__brow">Learning Resources</span> <div class="content__header"> <h1 class="content__title "> Curated Notebooks </h1> </div> <div class="content__body"> <p>Here you'll find a series of instructive and educational notebooks organized by topic areas.</p> </div> </div> </div> </div> </div> </div> </section><section class="group group--default " > <div class="group__anchor" id="gemini" style="transform: translateY(-20px)"></div> <div class="group__content-wrapper"> <div class="content group__content " > <div class="content__text" > <div class="content__header"> <h2 class="content__title "> Gemini API </h2> </div> <div class="content__body"> </div> </div> </div> </div> <div class="group__gallery-wrapper"> <div class="gallery gallery--image-card gallery--2-rows " > <div class="gallery__wrapper"> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/google-gemini/cookbook/blob/main/examples/Market_a_Jet_Backpack.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/google-gemini/cookbook/blob/main/examples/Market_a_Jet_Backpack.ipynb" target="_blank" title="Create a marketing campaign"><span class="hidden-text">Create a marketing campaign</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Create a marketing campaign </div> </div> <div class="content__body"> <p>This notebook contains an example of using the Gemini API to analyze a a product sketch (in this case, a drawing of a Jet Backpack), create a marketing campaign for it, and output taglines in JSON format.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/Audio.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/Audio.ipynb" target="_blank" title="Analyze audio recordings"><span class="hidden-text">Analyze audio recordings</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Analyze audio recordings </div> </div> <div class="content__body"> <p>This notebook provides an example of how to prompt Gemini 1.5 Pro using an audio file. In this case, you'll use a sound recording of President John F. Kennedy’s 1961 State of the Union address.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/System_instructions.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/System_instructions.ipynb" target="_blank" title="Use System instructions in chat"><span class="hidden-text">Use System instructions in chat</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Use System instructions in chat </div> </div> <div class="content__body"> <p>System instructions allow you to steer the behavior of the model. By setting the system instruction, you are giving the model additional context to understand the task, provide more customized responses, and adhere to guidelines over the user interaction.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/Function_calling_config.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/Function_calling_config.ipynb" target="_blank" title="Function calling"><span class="hidden-text">Function calling</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Function calling </div> </div> <div class="content__body"> <p>Using function calling allows you to control how the Gemini API acts when tools have been specified.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/google-gemini/cookbook/blob/main/examples/Apollo_11.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/google-gemini/cookbook/blob/main/examples/Apollo_11.ipynb" target="_blank" title="Prompting with a text file"><span class="hidden-text">Prompting with a text file</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Prompting with a text file </div> </div> <div class="content__body"> <p>This notebook provides a quick example of how to prompt Gemini 1.5 Pro using a text file. In this case, you'll use a 400 page transcript from Apollo 11.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/googlecolab/colabtools/blob/main/notebooks/Learning_with_Gemini_and_ChatGPT.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/googlecolab/colabtools/blob/main/notebooks/Learning_with_Gemini_and_ChatGPT.ipynb" target="_blank" title="Compare Gemini and ChatGPT responses"><span class="hidden-text">Compare Gemini and ChatGPT responses</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Compare Gemini and ChatGPT responses </div> </div> <div class="content__body"> <p>Use Google's latest model release, Gemini, to teach you what you want to know and compare those with ChatGPT's responses. The models are specifically prompted not to generate extra text to make it easier to compare any differences.</p> </div> </div> </div> </div> </div> </div> </div> </div> </section><section class="group group--default " > <div class="group__anchor" id="aiml" style="transform: translateY(-20px)"></div> <div class="group__content-wrapper"> <div class="content group__content " > <div class="content__text" > <div class="content__header"> <h2 class="content__title "> AI & Machine Learning </h2> </div> <div class="content__body"> </div> </div> </div> </div> <div class="group__gallery-wrapper"> <div class="gallery gallery--image-card gallery--2-rows " > <div class="gallery__wrapper"> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/GoogleCloudPlatform/training-data-analyst/blob/bd8362a940347fee80bf8b2785662536fd0a68a4/self-paced-labs/gemini/inspect_rich_documents_w_gemini_multimodality_and_multimodal_rag.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/GoogleCloudPlatform/training-data-analyst/blob/bd8362a940347fee80bf8b2785662536fd0a68a4/self-paced-labs/gemini/inspect_rich_documents_w_gemini_multimodality_and_multimodal_rag.ipynb" target="_blank" title="Inspect Rich Documents with Gemini Multimodality and Multimodal RAG"><span class="hidden-text">Inspect Rich Documents with Gemini Multimodality and Multimodal RAG</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Inspect Rich Documents with Gemini Multimodality and Multimodal RAG </div> </div> <div class="content__body"> <p>Use this self-paced lab from Google Cloud to inspect rich documents with Gemini.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/magenta/mt3/blob/main/mt3/colab/music_transcription_with_transformers.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/magenta/mt3/blob/main/mt3/colab/music_transcription_with_transformers.ipynb" target="_blank" title="Music Transcription with Transformers"><span class="hidden-text">Music Transcription with Transformers</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Music Transcription with Transformers </div> </div> <div class="content__body"> <p>Interactive demo of a few music transcription models created by Google's Magenta team. You can upload audio and have one of our models automatically transcribe it.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/notebooks/magenta/piano_transformer/piano_transformer.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/notebooks/magenta/piano_transformer/piano_transformer.ipynb" target="_blank" title="Generating Music with Transformers"><span class="hidden-text">Generating Music with Transformers</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Generating Music with Transformers </div> </div> <div class="content__body"> <p>This Colab notebook lets you play with pretrained Transformer models for piano music generation, based on the Music Transformer model introduced by Huang et al. in 2018.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_text_classification.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_text_classification.ipynb" target="_blank" title="Text Classification with Movie Reviews"><span class="hidden-text">Text Classification with Movie Reviews</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Text Classification with Movie Reviews </div> </div> <div class="content__body"> <p>This notebook classifies movie reviews as positive or negative using the text of the review. This is an example of binary—or two-class—classification, an important and widely applicable kind of machine learning problem.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/retrieval_with_tf_hub_universal_encoder_qa.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/retrieval_with_tf_hub_universal_encoder_qa.ipynb" target="_blank" title="Multilingual Universal Sentence Encoder Q&A Retrieval"><span class="hidden-text">Multilingual Universal Sentence Encoder Q&A Retrieval</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Multilingual Universal Sentence Encoder Q&A Retrieval </div> </div> <div class="content__body"> <p>Demo for using Universal Encoder Multilingual Q&A model for question-answer retrieval of text, illustrating the use of question_encoder and response_encoder of the model.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/google/dopamine/blob/master/dopamine/colab/agents.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/google/dopamine/blob/master/dopamine/colab/agents.ipynb" target="_blank" title="Create and Train a Custom RL Agent"><span class="hidden-text">Create and Train a Custom RL Agent</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Create and Train a Custom RL Agent </div> </div> <div class="content__body"> <p>This colab demonstrates how to create a variant of a provided agent (Example 1) and how to create a new agent from scratch (Example 2).</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/google/dopamine/blob/master/dopamine/colab/tensorboard.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/google/dopamine/blob/master/dopamine/colab/tensorboard.ipynb" target="_blank" title="Visualize RL Agent Training on TensorBoard"><span class="hidden-text">Visualize RL Agent Training on TensorBoard</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Visualize RL Agent Training on TensorBoard </div> </div> <div class="content__body"> <p>This colab allows you to easily view the trained baselines with Tensorboard (even if you don't have Tensorboard on your local machine!). Simply specify the game you would like to visualize and then run the cells in order.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/tensorflow/tensorboard/blob/master/docs/hyperparameter_tuning_with_hparams.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/tensorflow/tensorboard/blob/master/docs/hyperparameter_tuning_with_hparams.ipynb" target="_blank" title="Hyperparameter Tuning with Tensorboard"><span class="hidden-text">Hyperparameter Tuning with Tensorboard</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Hyperparameter Tuning with Tensorboard </div> </div> <div class="content__body"> <p>The HParams dashboard in TensorBoard provides several tools to help with this process of identifying the best experiment or most promising sets of hyperparameters.</p> </div> </div> </div> </div> </div> </div> </div> </div> </section><section class="group group--default " > <div class="group__anchor" id="data" style="transform: translateY(-20px)"></div> <div class="group__content-wrapper"> <div class="content group__content " > <div class="content__text" > <div class="content__header"> <h2 class="content__title "> Data & Analytics </h2> </div> <div class="content__body"> </div> </div> </div> </div> <div class="group__gallery-wrapper"> <div class="gallery gallery--image-card gallery--2-rows " > <div class="gallery__wrapper"> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/rapidsai-community/showcase/blob/main/getting_started_tutorials/cudf_pandas_colab_demo.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/rapidsai-community/showcase/blob/main/getting_started_tutorials/cudf_pandas_colab_demo.ipynb" target="_blank" title="10 Minutes to RAPIDS cuDF's pandas accelerator mode"><span class="hidden-text">10 Minutes to RAPIDS cuDF's pandas accelerator mode</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> 10 Minutes to RAPIDS cuDF's pandas accelerator mode </div> </div> <div class="content__body"> <p>cuDF is a Python GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating tabular data using a DataFrame style API in the style of pandas.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.11-Working-with-Time-Series.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.11-Working-with-Time-Series.ipynb" target="_blank" title="Working with time series in Python"><span class="hidden-text">Working with time series in Python</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Working with time series in Python </div> </div> <div class="content__body"> <p>This notebook introduces how to work with timestamps, time intervals, periods, time deltas, and durations.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/Tanu-N-Prabhu/Python/blob/master/Exploratory_data_Analysis.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/Tanu-N-Prabhu/Python/blob/master/Exploratory_data_Analysis.ipynb" target="_blank" title="Exploratory Data Analysis Intro"><span class="hidden-text">Exploratory Data Analysis Intro</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Exploratory Data Analysis Intro </div> </div> <div class="content__body"> <p>Getting started with data analysis on colab using python</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://github.com/firmai/business-analytics-and-mathematics-python-book" data-card-type="card"> <a class="card__link" href="https://github.com/firmai/business-analytics-and-mathematics-python-book" target="_blank" title="Advanced Business Analytics and Mathematics"><span class="hidden-text">Advanced Business Analytics and Mathematics</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Advanced Business Analytics and Mathematics </div> </div> <div class="content__body"> <p>Programmatic Google Colab Notebook Series (2018-2023)</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/drive/1WIcVZgbrU0DYOQqaxuaCLKY6CoLBV18O" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/drive/1WIcVZgbrU0DYOQqaxuaCLKY6CoLBV18O" target="_blank" title="Twitter Pulse Checker"><span class="hidden-text">Twitter Pulse Checker</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Twitter Pulse Checker </div> </div> <div class="content__body"> <p>This is a quick and dirty way to get a sense of what's trending on Twitter related to a particular Topic. For my use case, I am focusing on the city of Seattle but you can easily apply this to any topic.</p> </div> </div> </div> </div> </div> </div> </div> </div> </section><section class="group group--default " > <div class="group__anchor" id="cloud" style="transform: translateY(-20px)"></div> <div class="group__content-wrapper"> <div class="content group__content " > <div class="content__text" > <div class="content__header"> <h2 class="content__title "> Cloud Computing </h2> </div> <div class="content__body"> </div> </div> </div> </div> <div class="group__gallery-wrapper"> <div class="gallery gallery--image-card gallery--1-row " > <div class="gallery__wrapper"> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/drive/1hSI1BXyCyj7viRpp1GFZqkU1qtBUd0g1?authuser=0" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/drive/1hSI1BXyCyj7viRpp1GFZqkU1qtBUd0g1?authuser=0" target="_blank" title="Colab + BigQuery — Perfect Together"><span class="hidden-text">Colab + BigQuery — Perfect Together</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Colab + BigQuery — Perfect Together </div> </div> <div class="content__body"> <p>The goal of this Colab notebook is to highlight some benefits of using Google BigQuery and Colab together to perform some common data science tasks.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/bigquery_ml/bqml-online-prediction.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/bigquery_ml/bqml-online-prediction.ipynb" target="_blank" title="Online prediction with BigQuery ML"><span class="hidden-text">Online prediction with BigQuery ML</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Online prediction with BigQuery ML </div> </div> <div class="content__body"> <p>In this tutorial, you learn how to train and deploy a churn prediction model for real-time inference, with the data in BigQuery and model trained using BigQuery ML, registered to Vertex AI Model Registry, and deployed to an endpoint on Vertex AI for online predictions.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/prediction/pytorch_image_classification_with_prebuilt_serving_containers.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/prediction/pytorch_image_classification_with_prebuilt_serving_containers.ipynb" target="_blank" title="Serving PyTorch image models with prebuilt containers on Vertex AI"><span class="hidden-text">Serving PyTorch image models with prebuilt containers on Vertex AI</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Serving PyTorch image models with prebuilt containers on Vertex AI </div> </div> <div class="content__body"> <p>In this tutorial, you learn how to package and deploy a PyTorch image classification model using a prebuilt Vertex AI container with TorchServe for serving online and batch predictions.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb" target="_blank" title="AutoML training tabular binary classification model for batch explanation"><span class="hidden-text">AutoML training tabular binary classification model for batch explanation</span></a> <div class="card__image" data-gen-art-background></div> <div class="content content--long-title " > <div class="content__text" > <div class="content__header"> <div class="content__title "> AutoML training tabular binary classification model for batch explanation </div> </div> <div class="content__body"> <p>In this tutorial, you learn to use AutoML to create a tabular binary classification model from a Python script, and then learn to use Vertex AI Batch Prediction to make predictions with explanations.</p> </div> </div> </div> </div> </div> </div> </div> </div> </section><section class="group group--default " > <div class="group__anchor" id="dataviz" style="transform: translateY(-20px)"></div> <div class="group__content-wrapper"> <div class="content group__content " > <div class="content__text" > <div class="content__header"> <h2 class="content__title "> Data Visualization </h2> </div> <div class="content__body"> </div> </div> </div> </div> <div class="group__gallery-wrapper"> <div class="gallery gallery--image-card gallery--1-row " > <div class="gallery__wrapper"> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://cloud.google.com/blog/products/data-analytics/expanding-your-patent-set-with-ml-and-bigquery" data-card-type="card"> <a class="card__link" href="https://cloud.google.com/blog/products/data-analytics/expanding-your-patent-set-with-ml-and-bigquery" target="_blank" title="Explore Patent Database with ML"><span class="hidden-text">Explore Patent Database with ML</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Explore Patent Database with ML </div> </div> <div class="content__body"> <p>Patent landscaping is an analytical approach commonly used by corporations, patent offices, and academics to better understand the potential technical coverage of a large number of patents where manual review (i.e., actually reading the patents) is not feasible due to time or cost constraints.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/google/mediapy/blob/main/mediapy_examples.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/google/mediapy/blob/main/mediapy_examples.ipynb" target="_blank" title="mediapy"><span class="hidden-text">mediapy</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> mediapy </div> </div> <div class="content__body"> <p>Read, write, and show images and videos in a Colab notebook</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/vinayak2019/python_quantum_chemistry_introductory/blob/main/Input_structure_for_QC_calculations.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/vinayak2019/python_quantum_chemistry_introductory/blob/main/Input_structure_for_QC_calculations.ipynb" target="_blank" title="Visualize Chemical Structures in a Notebook"><span class="hidden-text">Visualize Chemical Structures in a Notebook</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Visualize Chemical Structures in a Notebook </div> </div> <div class="content__body"> <p>Molecules can be represented as strings with SMILES. Simplified molecular-input line-entry system (SMILES) is a string based representation of a molecule.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/Tanu-N-Prabhu/Python/blob/master/Exploratory_data_Analysis.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/Tanu-N-Prabhu/Python/blob/master/Exploratory_data_Analysis.ipynb" target="_blank" title="Exploratory Data Analysis with Python"><span class="hidden-text">Exploratory Data Analysis with Python</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Exploratory Data Analysis with Python </div> </div> <div class="content__body"> <p>Exploratory Data Analysis or (EDA) is understanding the data sets by summarizing their main characteristics and, usually, plotting them visually.</p> </div> </div> </div> </div> </div> </div> </div> </div> </section><section class="group group--default " > <div class="group__anchor" id="edu" style="transform: translateY(-20px)"></div> <div class="group__content-wrapper"> <div class="content group__content " > <div class="content__text" > <div class="content__header"> <h2 class="content__title "> Education </h2> </div> <div class="content__body"> </div> </div> </div> </div> <div class="group__gallery-wrapper"> <div class="gallery gallery--image-card gallery--1-row " > <div class="gallery__wrapper"> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/google/picatrix/blob/main/notebooks/Quick_Primer_on_Colab_Jupyter.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/google/picatrix/blob/main/notebooks/Quick_Primer_on_Colab_Jupyter.ipynb" target="_blank" title="Colab Primer"><span class="hidden-text">Colab Primer</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Colab Primer </div> </div> <div class="content__body"> <p>Quick primer on Colab and Jupyter notebooks</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/cs231n/cs231n.github.io/blob/master/python-colab.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/cs231n/cs231n.github.io/blob/master/python-colab.ipynb" target="_blank" title="Intro Python Tutorial"><span class="hidden-text">Intro Python Tutorial</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Intro Python Tutorial </div> </div> <div class="content__body"> <p>Stanford CS231n Python Tutorial With Google Colab</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/drive/1gCqFEquqNvEoTDX3SNhR2PZkXWPHKXnc?usp=sharing" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/drive/1gCqFEquqNvEoTDX3SNhR2PZkXWPHKXnc?usp=sharing" target="_blank" title="Advanced Python Tutorial"><span class="hidden-text">Advanced Python Tutorial</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Advanced Python Tutorial </div> </div> <div class="content__body"> <p>In this tutorial, we will be exploring some advanced Python concepts and techniques using Google Colab.</p> </div> </div> </div> </div> </div> </div> </div> </div> </section><section class="group group--default " > <div class="group__anchor" id="fun" style="transform: translateY(-20px)"></div> <div class="group__content-wrapper"> <div class="content group__content " > <div class="content__text" > <div class="content__header"> <h2 class="content__title "> Fun </h2> </div> <div class="content__body"> </div> </div> </div> </div> <div class="group__gallery-wrapper"> <div class="gallery gallery--image-card gallery--1-row " > <div class="gallery__wrapper"> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/tf2_arbitrary_image_stylization.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/tf2_arbitrary_image_stylization.ipynb" target="_blank" title="Fast Style Transfer for Arbitrary Styles"><span class="hidden-text">Fast Style Transfer for Arbitrary Styles</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Fast Style Transfer for Arbitrary Styles </div> </div> <div class="content__body"> <p>Based on the model code in magenta and the publication: Exploring the structure of a real-time, arbitrary neural artistic stylization network.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/google/brax/blob/main/notebooks/basics.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/google/brax/blob/main/notebooks/basics.ipynb" target="_blank" title="Brax - Physics Environments for Simulations"><span class="hidden-text">Brax - Physics Environments for Simulations</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Brax - Physics Environments for Simulations </div> </div> <div class="content__body"> <p>Brax simulates physical systems made up of rigid bodies, joints, and actutators.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/tensorflow/tpu/blob/master/tools/colab/shakespeare_with_tpu_and_keras.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/tensorflow/tpu/blob/master/tools/colab/shakespeare_with_tpu_and_keras.ipynb" target="_blank" title="Predict Shakespeare with Keras+CloudTPU"><span class="hidden-text">Predict Shakespeare with Keras+CloudTPU</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Predict Shakespeare with Keras+CloudTPU </div> </div> <div class="content__body"> <p>This example uses tf.keras to build a language model and train it on a Cloud TPU. This language model predicts the next character of text given the text so far. The trained model can generate new snippets of text that read in a similar style to the text training data.</p> </div> </div> </div> </div> </div> </div> </div> </div> </section><section class="group group--default " > <div class="group__anchor" id="science" style="transform: translateY(-20px)"></div> <div class="group__content-wrapper"> <div class="content group__content " > <div class="content__text" > <div class="content__header"> <h2 class="content__title "> Science </h2> </div> <div class="content__body"> </div> </div> </div> </div> <div class="group__gallery-wrapper"> <div class="gallery gallery--image-card gallery--1-row " > <div class="gallery__wrapper"> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/deepmind/alphafold/blob/master/notebooks/AlphaFold.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/deepmind/alphafold/blob/master/notebooks/AlphaFold.ipynb" target="_blank" title="AlphaFold"><span class="hidden-text">AlphaFold</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> AlphaFold </div> </div> <div class="content__body"> <p>This Colab notebook allows you to easily predict the structure of a protein using a slightly simplified version of AlphaFold v2.3.2.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/deepmind/alphatensor/blob/master/nonequivalence/inspect_factorizations_notebook.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/deepmind/alphatensor/blob/master/nonequivalence/inspect_factorizations_notebook.ipynb" target="_blank" title="AlphaTensor"><span class="hidden-text">AlphaTensor</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> AlphaTensor </div> </div> <div class="content__body"> <p>This Colab shows how to load the provided .npz file with rank- 49 factorizations of 𝓣4 in standard arithmetic, and how to compute the invariants ℛ and 𝒦 in order to demonstrate that these factorizations are mutually nonequivalent.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/google/earthengine-api/blob/master/python/examples/ipynb/ee-api-colab-setup.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/google/earthengine-api/blob/master/python/examples/ipynb/ee-api-colab-setup.ipynb" target="_blank" title="Google Earth API"><span class="hidden-text">Google Earth API</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Google Earth API </div> </div> <div class="content__body"> <p>This notebook demonstrates how to setup the Earth Engine Python API in Colab and provides several examples of how to print and visualize Earth Engine processed data.</p> </div> </div> </div> </div> </div> <div class="gallery__item"> <div class="card card--default " data-gen-art data-id="https://colab.research.google.com/github/pablo-arantes/Making-it-rain/blob/main/Amber.ipynb" data-card-type="card"> <a class="card__link" href="https://colab.research.google.com/github/pablo-arantes/Making-it-rain/blob/main/Amber.ipynb" target="_blank" title="Molecular Dynamics Simulations"><span class="hidden-text">Molecular Dynamics Simulations</span></a> <div class="card__image" data-gen-art-background></div> <div class="content " > <div class="content__text" > <div class="content__header"> <div class="content__title "> Molecular Dynamics Simulations </div> </div> <div class="content__body"> <p>Notebook for running Molecular Dynamics (MD) simulations using OpenMM engine and AMBER force field for PROTEIN systems. This notebook is a supplementary material of the paper "Making it rain: Cloud-based molecular simulations for everyone" (link here) and we encourage you to read it before using this pipeline.</p> </div> </div> </div> </div> </div> </div> </div> </div> </section> </main> <footer> <a href="https://www.google.com" target="_blank" aria-label="Google" class="footer__logo"> <svg role="img" aria-hidden="true" viewBox="0 0 396 130"> <path d="M51.0745265,101.038701 C23.3283097,101.038701 9.65724009e-07,78.4212338 9.65724009e-07,50.645974 C-0.00548030982,22.8707143 23.3228284,0.253246753 51.0745265,0.253246753 C66.4220981,0.253246753 77.3517615,6.27798701 85.5736748,14.1408766 L75.8718171,23.8528896 C69.9794459,18.3219805 61.9987087,14.0201623 51.0690452,14.0201623 C30.8102508,14.0201623 14.9693645,30.365974 14.9693645,50.645974 C14.9693645,70.925974 30.8102508,87.2717857 51.0690452,87.2717857 C64.2076627,87.2717857 71.6950851,81.9877922 76.48572,77.1921429 C80.4157945,73.2579545 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