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cnn-wpdp-appsci2019

dataset-CNN-WPDP_APPLSCI2019

cnns

Different kinds CNNs code(including kears,tf and pytorch)

code-of-learn-deep-learning-with-pytorch

This is code of book "Learn Deep Learning with PyTorch"

code2seq

Code for the model presented in the paper: "code2seq: Generating Sequences from Structured Representations of Code"

compare_gan

Compare GAN code.

coursera-ml-andrewng-notes

吴恩达老师的机器学习课程个人笔记

cpp_new_features

2021年最新整理， C++ 学习资料，含C++ 11 / 14 / 17 / 20 / 23 新特性、入门教程、推荐书籍、优质文章、学习笔记、教学视频等

cs-books-store

你想要的计算机经典书籍，这里都有！

ctr

CTR模型代码和学习笔记总结

ctr_nn

基于深度学习的CTR预估，从FM推演各深度学习CTR预估模型（附代码）

cwcf

Source code for paper Classification with Costly Features using Deep Reinforcement Learning.

data-competition-topsolution

Data competition Top Solution 数据竞赛top解决方案开源整理

dbn_learning

利用深度RBM构建多分类模型

dbn_stock

DBN股票预测的，改的程序，加了训练方法和tensorboard可视化等

dbnpytorch

deep-ctr-prediction

CTR prediction models based on deep learning(基于深度学习的广告推荐CTR预估模型)

deep-learning-interview-book

深度学习面试宝典（含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向）

deep-review-sharing

deep-siamese-text-similarity

Tensorflow based implementation of deep siamese LSTM network to capture phrase/sentence similarity using character/word embeddings

deep_gcns

Tensorflow Repo for "DeepGCNs: Can GCNs Go as Deep as CNNs?" ICCV2019 Oral https://deepgcns.org

deep_recommenders

Deep Recommenders

deepctr

Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

deepctr_tensorflow2

Deep-learning based CTR models implemented with tensorflow2.0. / 使用tensorflow2.0实现的一些基于深度学习的CTR模型

deeplearning

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

deeplearning_lhy21_notes

深度学习 李宏毅 2021 学习笔记

deepsim

defect-prediction

Sonar plugin that predicts which source files contain the most defects

defectdata

An R package of Defect Prediction Datasets for Software Engineering Research

defector

defect prediction using DBN generated semantic features by https://ece.uwaterloo.ca/~lintan/publications/deeplearn-icse16.pdf

detectandtrack

The implementation of an algorithm presented in the CVPR18 paper: "Detect-and-Track: Efficient Pose Estimation in Videos"