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edex-ui

A cross-platform, customizable science fiction terminal emulator with advanced monitoring & touchscreen support.

eesen

End-to-End Speech Recognition using Deep RNNs (Models), CTC (Training) and WFSTs (Decoding)

elephas

Distributed Deep learning with Keras & Spark

espnet

End-to-End Speech Processing Toolkit

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

giantmidi-piano

gmm-on-hadoop

Running GMM Training Algorithm (EM) on Hadoop Clusters

gmr

Gaussian Mixture Regression

google-research

Google Research

groundhog

Library for implementing RNNs with Theano

halide

a language for fast, portable data-parallel computation

hey-jetson

Deep Learning based Automatic Speech Recognition with attention for the Nvidia Jetson.

high-quality-chat

Capstone Project for Georgia Tech

hmmlearn

Hidden Markov Models in Python, with scikit-learn like API

horovod

Distributed training framework for TensorFlow, Keras, and PyTorch.

htk_io

Read and write HTK and HTS files from python.

jetson-inference

Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.

jihanki

Jihanki is a lightweight WFST toolkit implemented entirely in python

juicer

Juicer is a Weighted Finite State Transducer (WFST) based decoder for Automatic Speech Recognition (ASR).

kaldi

This is now the official location of the Kaldi project.

kaldi-lstm

C++ implementation of LSTM (Long Short Term Memory), in Kaldi's nnet1 framework. Used for automatic speech recognition, possibly language modeling etc.

keras

Theano-based Deep Learning library (convnets, recurrent neural networks, and more).

keras-wavenet

Keras implementation of deepmind's wavenet paper

lasagne-ctc

CTC for lasagne

lhotse

lstm

LSTM Neural Network in Python and Cython, used for language modelling

matconvnet

MatConvNet: CNNs for MATLAB

memnn

midi-rnn

Generate monophonic melodies with machine learning using a basic LSTM RNN

mmdnn

MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.