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asneat-visualizer

A set of visualizations for an asNEAT network

asr-evaluation

Python module for evaluating ASR hypotheses (e.g. word error rate, word recognition rate).

asr-scripts

Lots of miscellaneous scripts to work with Sphinx ASR files and other projects in belambert.

asr-tools

Libraries and scripts for manipulating and handling ASR output/n-bests/etc.

asr_audio_data_links

A list of publically available audio data that anyone can download for ASR or other speech activities

asreader

This is an implementation of the Attention Sum Reader model as presented in "Text Comprehension with the Attention Sum Reader Network" available at http://arxiv.org/abs/1603.01547.

assessments

asset

资产服务器。

asset-price-prediction_machine-learning

Works with any open, high, low, close prices. It works with QuantConnect CSV data downloaded by default.

assetreturns_behavior

Statistical analysis of lognormal asset returns

assignment-bank

Contribute alternative assignments for Numerical Methods with Python

assistant-dialog-skill-analysis

Dialog Skill Analysis framework for Watson Assistant

assistant-improve-recommendations-notebook

assn_nbs

astar-matlab

An implementation of Grid search Astar (x,y,t) in matlab for prototyping purposes

astor

Python AST read/write

astral

Accurate Species TRee ALgorithm

astrobee

NASA Astrobee Robot Software

astrobee_android

NASA Astrobee Robot Software, Android

astroml

Machine learning, statistics, and data mining for astronomy and astrophysics

astropy-workshop

Materials for Astropy Workshop

asymmetric-hashing-ann

Implementation of Google's approximate nearest neighbor search method, described here: https://www.google.com/trends/correlate/nnsearch.pdf

async-deep-rl

A Tensorflow based implementation of "Asynchronous Methods for Deep Reinforcement Learning": https://arxiv.org/abs/1602.01783

async-rl

Tensorflow + Keras + OpenAI Gym implementation of 1-step Q Learning from "Asynchronous Methods for Deep Reinforcement Learning"

async-rl--grzego

Variation of "Asynchronous Methods for Deep Reinforcement Learning" with multiple processes generating experience for agent (Keras + Theano + OpenAI Gym)[1-step Q-learning, n-step Q-learning, A3C]

async-rl-muupan

Replicating "Asynchronous Methods for Deep Reinforcement Learning" (http://arxiv.org/abs/1602.01783)

async-rl-noreward

Keras implementation of Curiosity-driven Exploration by Self-supervised Prediction

async_deep_reinforce

Asynchronous Methods for Deep Reinforcement Learning

asynchronous-methods-for-deep-reinforcement-learning

Using a paper from Google DeepMind I've developed a new version of the DQN using threads exploration instead of memory replay as explain in here: http://arxiv.org/pdf/1602.01783v1.pdf I used the one-step-Q-learning pseudocode, and now we can train the Pong game in less than 20 hours and without any GPU or network distribution.

asyncio-tkinter

Example of running asyncio on top of the Tcl/Tk event loop via Tkinter