L9g's Stars
Harvard-IACS/2022-CS109B
Anduin2017/HowToCook
程序员在家做饭方法指南。Programmer's guide about how to cook at home (Simplified Chinese only).
croach/statistics-for-hackers
A Jupyter notebook to accompany Jake VanderPlas's "Statistics for Hackers" talk from PyCon 2016.
RichardYang40148/MidiNet
This repository contains the source code of MdidNet : A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
ratschlab/RGAN
Recurrent (conditional) generative adversarial networks for generating real-valued time series data.
laiguokun/LSTNet
jeffheaton/t81_558_deep_learning
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
patrickloeber/MLfromscratch
Machine Learning algorithm implementations from scratch.
Vay-keen/Machine-learning-learning-notes
周志华《机器学习》又称西瓜书是一本较为全面的书籍，书中详细介绍了机器学习领域不同类型的算法(例如：监督学习、无监督学习、半监督学习、强化学习、集成降维、特征选择等)，记录了本人在学习过程中的理解思路与扩展知识点，希望对新人阅读西瓜书有所帮助！
DiscoverML/simplified-deeplearning
Simplified implementations of deep learning related works
SimonOuellette35/RLNonStationary
Demo for the application of RL to non-stationary effects
vzhou842/cnn-from-scratch
A Convolutional Neural Network implemented from scratch (using only numpy) in Python.
Awesome-Interview/Awesome-Interview
Collection of awesome interview references.
eriklindernoren/ML-From-Scratch
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
rushter/MLAlgorithms
Minimal and clean examples of machine learning algorithms implementations
PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Bots-with-Python
JWarmenhoven/ISLR-python
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
borisbanushev/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.
JonnoFTW/traffic-prediction
Predict traffic flow with LSTM. For experimental purposes only, unsupported!
talkpython/100daysofcode-with-python-course
Course materials and handouts for #100DaysOfCode in Python course
jackfrued/Python-100-Days
Python - 100天从新手到大师
Avik-Jain/100-Days-Of-ML-Code
100 Days of ML Coding
996icu/996.ICU
Repo for counting stars and contributing. Press F to pay respect to glorious developers.
bestony/logoly
A Pornhub Flavour Logo Generator
mkramar/dizzyrobot
ShenZhenAccelerationTechCo/Tdrone
Open source coaxial drone
JingqingZ/BaiduTraffic
This repo includes introduction, code and dataset of our paper Deep Sequence Learning with Auxiliary Information for Traffic Prediction (KDD 2018).
Arturus/kaggle-web-traffic
1st place solution
kevinzakka/recurrent-visual-attention
A PyTorch Implementation of "Recurrent Models of Visual Attention"
fonnesbeck/Bios8366
Advanced Statistical Computing at Vanderbilt University Medical Center's Department of Biostatistics