magicbupt's repositories
pytorch-fm
Factorization Machine models in PyTorch
pytorch-handbook
pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行
adversarial-multi-criteria-learning-for-CWS
The implementation of paper https://arxiv.org/abs/1704.07556, ACL 2017
book
关于技术类的电子书，为了去中心化，保存在git上，不至于丢失，[侵删]
byteps
A high performance and general framework for distributed training
caffe
Caffe: a fast open framework for deep learning.
caravel
Caravel is a data exploration platform designed to be visual, intuitive, and interactive
DeepCTR-Torch
【PyTorch】Easy-to-use,Modular and Extendible package of deep-learning based CTR models.
DeepFM_with_PyTorch
A PyTorch implementation of DeepFM for CTR prediction problem.
faiss
A library for efficient similarity search and clustering of dense vectors.
folly
An open-source C++ library developed and used at Facebook.
GroundHog
Library for implementing RNNs with Theano
hadoop-1
Mirror of Apache Hadoop
kcws
Deep Learning Chinese Word Segment
libdnn
A lightweight and user friendly C++ library for deep and convolutional neural network with GPU acceleration
mt-rnn
An RNN based encoder-decoder model for Machine Translation
mxnet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more
mxnet-gtc-tutorial
MXNet Tutorial for NVidia GTC 2016.
mxnet-the-straight-dope
An interactive book on deep learning. Much easy, so MXNet. Wow.
mxnet-wgan
mxnet implement for Conditional Wasserstein GAN
neuraltalk
NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.
ps-lite
A lightweight parameter server interface
python-phonenumbers
Python port of Google's libphonenumber
PyTorch-GAN
PyTorch implementations of Generative Adversarial Networks.
ShuntingYard
A C++ library for parsing and running math expressions without all the hard work. Allows you to add your own functions and variables.
ufldl_tutorial
Stanford Unsupervised Feature Learning and Deep Learning Tutorial