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10_days_of_deep_learning icon 10_days_of_deep_learning

10 days 10 different practical applications of Deep Learning (primarily NLP) using Tensorflow and Keras

algorithm_interview_notes-chinese icon algorithm_interview_notes-chinese

2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记

artificial-intelligence-deep-learning-machine-learning-tutorials icon artificial-intelligence-deep-learning-machine-learning-tutorials

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)

awesome-python-cn icon awesome-python-cn

Python资源大全中文版,包括:Web框架、网络爬虫、模板引擎、数据库、数据可视化、图片处理等,由伯乐在线持续更新。

awesome-python3-webapp icon awesome-python3-webapp

小白的Python入门教程实战篇:网站+iOS App源码→ http://t.cn/R2PDyWN 赞助→ http://t.cn/R5bhVpf

awesome-pytorch-list icon awesome-pytorch-list

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

char-rnn icon char-rnn

Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch

chatbot icon chatbot

一个可以自己进行训练的中文聊天机器人, 根据自己的语料训练出自己想要的聊天机器人,可以用于智能客服、在线问答、智能聊天等场景。目前包含seq2seq、seqGAN版本和tf2.0版本。

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

deepnude_nowatermark_withmodel icon deepnude_nowatermark_withmodel

DeepNude源码,去水印,带三个模型文件下载地址 (cm.lib, mm.lib, mn.lib),附使用说明,离线运行,下载即跑,基于GAN的一行命令给小姐姐"换"衣服。

dive-into-dl-pytorch icon dive-into-dl-pytorch

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

drqa icon drqa

Reading Wikipedia to Answer Open-Domain Questions

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