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pengdeke's Projects

alphatree-graphic-deep-neural-network icon alphatree-graphic-deep-neural-network

AI Roadmap:机器学习(Machine Learning)、深度学习(Deep Learning)、对抗神经网络(GAN),图神经网络(GNN),NLP,大数据相关的发展路书(roadmap), 并附海量源码(python,pytorch)带大家消化基本知识点,突破面试,完成从新手到合格工程师的跨越,其中深度学习相关论文附有tensorflow caffe官方源码,应用部分含推荐算法和知识图谱

atrank icon atrank

An Attention-Based User Behavior Modeling Framework for Recommendation

awesome-programming-books icon awesome-programming-books

经典编程书籍大全,涵盖:计算机系统与网络、系统架构、算法与数据结构、前端开发、后端开发、移动开发、数据库、测试、项目与团队、程序员职业修炼、求职面试等

deeplearning-500-questions icon 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

fun-rec icon fun-rec

本推荐算法教程主要是针对具有机器学习基础并想找推荐算法岗位的同学,教程由推荐算法基础、推荐算法入门赛、新闻推荐项目及推荐算法面经组成,形成了一个完整的从基础到实战再到面试的闭环。

generative_models_tutorial_with_demo icon generative_models_tutorial_with_demo

Generative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..

hello-algorithm icon hello-algorithm

🌍 东半球最酷的学习项目 | 1、我写的三十万字算法图解 2、千本开源电子书 3、100 张思维导图 4、100 篇大厂面经 5、30 个学习专题 🚀 🚀 🚀 右上角点个 star,加入我们万人学习群!English Supported!

lstm_rnn_tutorials_with_demo icon lstm_rnn_tutorials_with_demo

LSTM-RNN Tutorial with LSTM and RNN Tutorial with Demo with Demo Projects such as Stock/Bitcoin Time Series Prediction, Sentiment Analysis, Music Generation using Keras-Tensorflow

ml-nlp icon ml-nlp

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现,也是作为一个算法工程师必会的理论基础知识。

python-small-examples icon python-small-examples

Python有趣的小例子一网打尽。Python基础、Python坑点、Python字符串和正则、Python绘图、Python日期和文件、Web开发、数据科学、机器学习、深度学习、TensorFlow、Pytorch,一切都是简单易懂的小例子。

spark-ctr-models icon spark-ctr-models

CTR prediction models based on spark(LR,FM、XGBoost、XGBoostLR、XGBoostFM)

stereogan icon stereogan

Depth estimation from stereo images using GAN

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