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adventures-in-deep-learning

A curated list of Deep Learning resources sorted by reputation

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

autoencoder-for-the-physical-layer

Using Keras to validate the simulation results according to Paper : "An Introduction to Deep Learning for the Physical Layer"

awesome_3dreconstruction_list

A curated list of papers & resources linked to 3D reconstruction from images.

barefoot

Java map matching library for integrating the map into software and services with state-of-the-art online and offline map matching that can be used stand-alone and in the cloud.

channelnet

Implementation of the paper "Deep Learning-Based Channel Estimation"

coding-interview-university

A complete computer science study plan to become a software engineer.

coursera-ml-andrewng-notes

吴恩达老师的机器学习课程个人笔记

dataflowr

code for deep learning courses

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deep-reinforcement-learning-for-dynamic-spectrum-access

Using multi-agent Deep Q Learning with LSTM cells (DRQN) to train multiple users in cognitive radio to learn to share scarce resource (channels) equally without communication

deepcom

[NOT OFFICIAL VERSION] Communication Algorithms via Deep Learning. Paper: https://arxiv.org/abs/1805.09317

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，近30万字。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_ai_books

deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）

deeplearningbook-chinese

Deep Learning Book Chinese Translation

deeplearningtutorial

Deep learning tutorial in Chinese/深度学习教程中文版

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。

geometric-constellation-shaping

halfrost-field

✍️ 这里是写博客的地方 —— Halfrost-Field 冰霜之地

hands-on-ml-zh

:book: [译] Sklearn 与 TensorFlow 机器学习实用指南

interpretablemlbook

《可解释的机器学习--黑盒模型可解释性理解指南》，该书为《Interpretable Machine Learning》中文版

interview-notebook

:books: 技术面试需要掌握的基础知识整理，欢迎编辑~

interview_internal_reference

2020年最新总结，阿里，腾讯，百度，美团，头条等技术面试题目，以及答案，专家出题人分析汇总。

json

JSON for Modern C++

keras-applications

Reference implementations of popular deep learning models.

keras-gan

Keras implementations of Generative Adversarial Networks.

keras-rl

Deep Reinforcement Learning for Keras.

mimo_ofdm

《MIMO-OFDM无线通信技术及MATLAB实现》随书源码

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。

models

Models and examples built with TensorFlow