Stars
Python Data Science Handbook: full text in Jupyter Notebooks
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…
Companion webpage to the book "Mathematics For Machine Learning"
The "Python Machine Learning (1st edition)" book code repository and info resource
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
Public facing notes page
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Automatic extraction of relevant features from time series:
Visualizations for machine learning datasets
Repository of teaching materials, code, and data for my data analysis and machine learning projects.
An adversarial example library for constructing attacks, building defenses, and benchmarking both
Simple tutorials using Google's TensorFlow Framework
From the basics to slightly more interesting applications of Tensorflow
A collection of infrastructure and tools for research in neural network interpretability.
Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier
Fast and flexible AutoML with learning guarantees.
A python library for decision tree visualization and model interpretation.
Simple chat program that communicates using inaudible sounds
Simplified implementations of deep learning related works
A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN