Gabriel B.C.

py4e. Web site for www.py4e.com and source to the Python 3.0 textbook

deep-rl-class. This repo contains the Hugging Face Deep Reinforcement Learning Course.

rl-baselines3-zoo. A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

manim. A community-maintained Python framework for creating mathematical animations.

smplx. SMPL-X

body_visualizer. Visualization code for SMPL body model Family

smplify-x. Expressive Body Capture: 3D Hands, Face, and Body from a Single Image

human_body_prior. VPoser: Variational Human Pose Prior

amass. Data preparation and loader for AMASS

moshpp. Motion and Shape Capture from Sparse Markers

skinning_decomposition_kavan. A clean Python reimplementation of "Fast and Efficient Skinning of Animated Meshes."

soma. Solving Optical MoCap Automatically

bvh-converter. Converts BVH file to joint location CSV

EpipolarPose. Self-Supervised Learning of 3D Human Pose using Multi-view Geometry (CVPR2019)

openpose. OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

bvh-python. Python module for parsing BVH (Biovision hierarchical data) mocap files

npybvh. Biovision Hierarchy (BVH) reader for python with numpy pose output

bvh-toolbox. Scripts for manipulating and converting BVH files.

building-machine-learning-pipelines. Code repository for the O'Reilly publication "Building Machine Learning Pipelines" by Hannes Hapke & Catherine Nelson

transformer. Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series.

koalas. Koalas: pandas API on Apache Spark

DockerSimpleDemo. A small set of examples to introduce the very basics of Docker

Tetris-deep-Q-learning-pytorch. Deep Q-learning for playing tetris game

agents. TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.

pingouin. Statistical package in Python based on Pandas

Datasets. Machine learning datasets used in tutorials on MachineLearningMastery.com

dlaicourse. Notebooks for learning deep learning

gp_regression. A Primer on Gaussian Processes for Regression Analysis (PyData NYC 2019)

altair. Declarative visualization library for Python

pymc4. (Deprecated) Experimental PyMC interface for TensorFlow Probability. Official work on this project has been discontinued.

fastai. The fastai deep learning library

machine_learning_examples. A collection of machine learning examples and tutorials.

DeepMind-Advanced-Deep-Learning-and-Reinforcement-Learning. Advanced Deep Learning and Reinforcement Learning course taught at UCL in partnership with Deepmind

go. The Open Source Data Science Masters

jupyter. Jupyter metapackage for installation and documentation

data-science-process-management. Resources for Data Science Process management

beyond-jupyter. 🐍💻📊 All material from the PyCon.DE 2018 Talk "Beyond Jupyter Notebooks - Building your own data science platform with Python & Docker" (incl. Slides, Video, Udemy MOOC & other References)

statrethinking_winter2019. Statistical Rethinking course at MPI-EVA from Dec 2018 through Feb 2019

stanford-cs-230-deep-learning. VIP cheatsheets for Stanford's CS 230 Deep Learning

pydata-networkx. [Archived! Check out http://ericmjl.github.io/Network-Analysis-Made-Simple/] A short tutorial on network analysis using Game of Thrones, US Airports and Python!

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers. 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 ;)

effective-pandas. Source code for my collection of articles on using pandas.

TensorFlow-Examples. TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

awesome-machine-learning. A curated list of awesome Machine Learning frameworks, libraries and software.

Machine-Learning-Tutorials. machine learning and deep learning tutorials, articles and other resources

Learn_Deep_Learning_in_6_Weeks. This is the Curriculum for "Learn Deep Learning in 6 Weeks" by Siraj Raval on Youtube

neural-networks-and-deep-learning. Code samples for my book "Neural Networks and Deep Learning"

deep-learning-with-python-notebooks. Jupyter notebooks for the code samples of the book "Deep Learning with Python"

keras-resources. Directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library

statsintro_python. Python modules and IPython Notebooks, for the book "Introduction to Statistics With Python"

lifetimes. Lifetime value in Python

workshops. A few exercises for use at events.

models. Models and examples built with TensorFlow

tensorflow. An Open Source Machine Learning Framework for Everyone