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100-days-of-ml-code

100 Days of ML Coding

3346_final

ailearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

aima-python

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

awesome-python3-webapp

廖雪峰教程实战

azure-kinect-mkv-reader

A simple Python 3 class for reading Azure Kinect DK MKV files, derived from vi/mkvparse

coding-test

comp5421_hw1

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

eit-kit_open-source

fyp

ganotebooks

wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch

generative-adversarial-networks

Introduction to generative adversarial networks, with code to accompany the O'Reilly tutorial on GANs

gitskills

interview-notebook

:books: 技术面试需要掌握的基础知识整理

learngit

learngit_on_thinkpad

lihang-code

《统计学习方法》的代码实现

machine-learning-for-algorithmic-trading-second-edition_original

Machine Learning for Algorithmic Trading, Second Edition - published by Packt

machine-learning-for-trading

Code and resources for Machine Learning for Algorithmic Trading, 2nd edition.

multi-task-learning-pytorch

PyTorch implementation of multi-task learning architectures, incl. MTI-Net (ECCV2020).

proxylessnas

[ICLR 2019] ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware.

python-guide

Python best practices guidebook, written for humans.

recsys

项亮的《推荐系统实践》的代码实现

recsys-1

推荐系统实践(基于近邻和LFM的推荐系统)

rfnet

RF-Net: A Unified Meta-Learning Framework for RF-enabledOne-Shot Human Activity Recognition

serial_port_master_machine

HKUST and SYSU

stocknet-dataset

A comprehensive dataset for stock movement prediction from tweets and historical stock prices.

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

the-gan-zoo

A list of all named GANs!