The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.

deeplearning.ai , By Andrew Ng, All slide and notebook + data + solutions and video link

This Python library makes it easy to display images and videos in a notebook.

Quick, visual, principled introduction to pytorch code through five colab notebooks.

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.

PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

Run a Jupyter Notebook to detect, track, and count cars in a video using PowerAI Vision and OpenCV

Code, Notebooks and Examples from Practical Business Python