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Python Data Science Handbook: full text in Jupyter Notebooks
A game theoretic approach to explain the output of any machine learning model.
Python programs, usually short, of considerable difficulty, to perfect particular skills.
From the basics to slightly more interesting applications of Tensorflow
Acceptance rates for the major AI conferences
A collection of infrastructure and tools for research in neural network interpretability.
Repo for the Deep Learning Nanodegree Foundations program.
Solve puzzles. Improve your pytorch.
3D U-Net model for volumetric semantic segmentation written in pytorch
Notebooks about Bayesian methods for machine learning
Various ipython notebooks
Keras implementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet
Practical Cheminformatics Tutorials
TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
A pytorch implementation of the vector quantized variational autoencoder (https://arxiv.org/abs/1711.00937)
Practical assignments of the Deep|Bayes summer school 2019
A dataset of datasets for learning to learn from few examples
Pytorch easy-to-follow Capsule Network tutorial
Artificial Intelligence Research for Science (AIRS)
Ways of doing Data Science Engineering and Machine Learning in R and Python
Dataset to assess the disentanglement properties of unsupervised learning methods
MIMIC-Extract:A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III
Machine learning notebooks in different subjects optimized to run in google collaboratory
Machine Learning Lectures at the European Space Agency (ESA) in 2018
GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process.
This is a tutorial to connect the fundamental mathematics to a practical implementation addressing the continual learning problem of artificial intelligence
Revisions and implementations of modern Convolutional Neural Networks architectures in TensorFlow and Keras
Lecture slides, Jupyter notebooks, and other material from the LSSTC Data Science Fellowship Program