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adversarial-multi-criteria-learning-for-cws

The implementation of paper https://arxiv.org/abs/1704.07556, ACL 2017

caffe

Caffe: a fast open framework for deep learning.

caffe2

Caffe2 is a lightweight, modular, and scalable deep learning framework.

cws_lstm

EMNLP2015_code_Long Short-Term Memory Neural Networks for Chinese Word Segmentation

deepqa

My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

eacl2017_mtl

Code for EACL '17 paper "Identifying beneficial task relations for multi-task learning in deep neural networks"

fancyimpute

Multivariate imputation and matrix completion algorithms implemented in Python

gpflow

Gaussian processes in TensorFlow

gpflowopt

Bayesian Optimization using GPflow

gpytorch

A highly efficient and modular implementation of Gaussian Processes in PyTorch

handong1587.github.io

hyperopt

Distributed Asynchronous Hyperparameter Optimization in Python

hyperopt-sklearn

Hyper-parameter optimization for sklearn

lbfgsb-matlab

A MATLAB interface for L-BFGS-B

learning-to-drive-in-a-day

Implementation of reinforcement learning approach to make a car learn to drive

long-range-extrapolation

Capturing Structure Implicitly from Noisy Time-Series having Limited Data

missingno

Missing data visualization module for Python.

models

Models and examples built with TensorFlow

mosm

Multi-Output Spectral Mixture kernel

nccl

Optimized primitives for collective multi-GPU communication

nlpcc2017_news_headline_categorization

NLPCC2017示例代码以及数据描述

nonstationary-spectral-kernels

Implementation for Non-stationary Spectral Kernels (NIPS 2017)

pillow-simd

The friendly PIL fork

pmtk3

Probabilistic Modeling Toolkit for Matlab/Octave.

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 ;)

promp

ProMP: Proximal Meta-Policy Search

scikit-learn

scikit-learn: machine learning in Python

spearmint

Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Larochelle and Ryan P. Adams. Advances in Neural Information Processing Systems, 2012

tensorflow-nre

Neural Relation Extraction implemented with LSTM in TensorFlow

tensorflow_cookbook

Code for Tensorflow Machine Learning Cookbook