kesyren Goto Github PK

adversarialnetspapers

The classical paper list with code about generative adversarial nets

andrew-ng-machine-learning-notes

The offical notes of Andrew Ng Machine Learning in Stanford University

bid-optimization

chatlearner

A simple chatbot implemented in TensorFlow based on the seq2seq model, with certain rules integrated.

coarse2fine

code-detecting-causal-structure-on-cloud-application-microservices-using-granger-causality-models

copulas

A library to model multivariate data using copulas.

cs228-notes

Course notes for CS228: Probabilistic Graphical Models.

data_efficient_grammar

[ICLR 2022] Data-Efficient Graph Grammar Learning for Molecular Generation

deep-learning

Repo for the Deep Learning Nanodegree Foundations program.

deep-learning-python

PyTorch and TensorFlow for deep learning

deep-reinforcement-learning-hands-on

Hands-on Deep Reinforcement Learning, published by Packt

deeplearning

深度学习入门教程, 优秀文章, Deep Learning Tutorial

delving-deep-into-gans

Generative Adversarial Networks (GANs) resources sorted by citations

gspan

Python implementation of frequent subgraph mining algorithm gSpan. Directed graphs are supported.

kdrg-dl-meeting

Paper

kesyren.github.io

Qing Wang's Homepage

leetcode-linghu-templete

算法面试必备，推荐刷题网站www.lintcode.com。北大学霸的《LeetCode刷题模板》+V领取: jiuzhangfeifei

machine_translation

mit-deep-learning-book-pdf

MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

neural-symbolic-machines

Neural Symbolic Machines is a framework to integrate neural networks and symbolic representations using reinforcement learning, with applications in program synthesis and semantic parsing.

nmt

TensorFlow Neural Machine Translation Tutorial

ntm_back_translation

pmf

seqgan

A simplified PyTorch implementation of "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient." (Yu, Lantao, et al.)

skillsnetwork

tutorials

机器学习相关教程

word2vec

My own comments and modifications to word2vec by Mikolov et al.