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fast-wavenet

Speedy Wavenet generation using dynamic programming :zap:

ga3c

Hybrid CPU/GPU implementation of the A3C algorithm for deep reinforcement learning.

ggbiplot

A biplot based on ggplot2

google-cloud-swarm

Scripts to create and manage a Docker Swarm cluster on Google Cloud Platform

graphpipe

Machine Learning Model Deployment Made Simple

grid-lstm-tensorflow

Examples of using GridLSTM (and GridRNN in general) in tensorflow

gym

A toolkit for developing and comparing reinforcement learning algorithms.

gym-trading

Environment for reinforcement-learning algorithmic trading models

hft-orderbook

Limit Order Book for high-frequency trading (HFT), as described by WK Selph, implemented in Python3 and C

how-to-check-if-time-series-data-is-stationary-with-python

How to Check if Time Series Data is Stationary with Python

kalman-and-bayesian-filters-in-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 filters, and more. All exercises include solutions.

keras-tcn

Keras Temporal Convolutional Network.

kolmogorov-zurbenko-filter

Python implementation of the Kolmogorov Zurbenko filter

learning-to-learn

Learning to Learn in TensorFlow

macos-tensorflowbuilds

Optimized tensorflow wheels binaries build for macos

maml_rl

Code for RL experiments in "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

meta-learning-papers

Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning

models

Models and examples built with TensorFlow

networkx

Official NetworkX source code repository.

nips2015_vrnn

noisynet-dqn

Tensorflow Implementation for "Noisy network for exploration"

orderbook

Matching Engine for Limit Order Book

ornstein-uhlenbeck-bayesian

padasip

Python Adaptive Signal Processing

practical_rl

A course in reinforcement learning in the wild

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 ;)

pybisp

This aims to be a collection of tools for performing Bayesian parameter estimation and model selection on stochastic processes. The immediate goal is to implement estimation methods

pyemd

Python implementation of Empirical Mode Decompoisition (EMD) method

pykalman

Kalman Filter, Smoother, and EM Algorithm for Python

pylimitorderbook

Limit Order Book Implemented in Python