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deep_learning_object_detection

A paper list of object detection using deep learning.

deeplearning

深度学习相关的模型训练、评估和预测相关代码

deeplearning-1

Python for《Deep Learning》，该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

deeplearning-models

A collection of various deep learning architectures, models, and tips

deeplearning_demo

深度学习入门的一些简单例子

deepmatch

A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search.

deepmcp

Deep Matching, Correlation and Prediction (DeepMCP) Model

deeprl

Deep Reinforcement Learning Lab, a platform designed to make DRL technology and fun for everyone

dien

dren

DREN:Deep Rotation Equivirant Network

dsin

Code for the IJCAI'19 paper "Deep Session Interest Network for Click-Through Rate Prediction"

dstn

Deep Spatio-Temporal Neural Network (DSTN)

dual

Code for paper "Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning"

easytransfer

EasyTransfer is designed to make the development of transfer learning in NLP applications easier.

es_dfm

code of our AAAI 2021 paper Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling

euler

A distributed graph deep learning framework.

faster_rcnn_pytorch

An reimplement of faster r-cnn(resnet) with pytorch

fastfcn

FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation.

fasttext

Library for fast text representation and classification.

fucking-algorithm

手把手撕LeetCode题目，扒各种算法套路的裤子。English version supported! Crack LeetCode, not only how, but also why.

gat

Graph Attention Networks (https://arxiv.org/abs/1710.10903)

gcn

Implementation of Graph Convolutional Networks in TensorFlow

geniepath-pytorch

This is a PyTorch implementation of the GeniePath model in <GeniePath: Graph Neural Networks with Adaptive Receptive Paths> (https://arxiv.org/abs/1802.00910)

gnn_review

GNN综述阅读报告

graphembedding

Implementation and experiments of graph embedding algorithms.deep walk,LINE(Large-scale Information Network Embedding),node2vec,SDNE(Structural Deep Network Embedding),struc2vec

graphsage

Representation learning on large graphs using stochastic graph convolutions.

icme2019_short_video_understanding_challenge_rank14

ICME2019&字节跳动 短视频内容理解与推荐竞赛rank14方案

interpret

Fit interpretable models. Explain blackbox machine learning.

interview_python

关于Python的面试题