machine learning - doubility-sky/daydayup GitHub Wiki

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.

• https://github.com/academic/awesome-datascience
• industry-machine-learning A curated list of applied machine learning and data science notebooks and libraries across different industries. https://www.linkedin.com/company/firmai

Machine Learning

• Simplest artificial neural network The simplest form of an artificial neural network explained and demonstrated.
• Machine Learning Crash Course - with TensorFlow APIs, Google's fast-paced, practical introduction to machine learning
• Machine Learning for Beginners - A Curriculum
• The Facebook Field Guide to Machine Learning is a six-part video series developed by the Facebook ads machine learning team. The series shares best real-world practices and provides practical tips about how to apply machine-learning capabilities to real-world problems.
• Top-down learning path, A complete daily plan for studying to become a machine learning engineer.
• Mathematics For Machine Learning, pdf
• Machine Learning Yearning 中文版 - 《机器学习训练秘籍》 - Andrew Ng 著
• ML-From-Scratch, Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.
• 100-Days-Of-ML-Code, 100 Days of Machine Learning Coding as proposed by Siraj Raval
• MLAlgorithms, A collection of minimal and clean implementations of machine learning algorithms.
• IBM/elasticsearch-spark-recommender, Use Jupyter Notebooks to demonstrate how to build a Recommender with Apache Spark & Elasticsearch
• interpretable-ml-book, Explaining the decisions and behaviour of machine learning models.
• Machine Learning Systems Design, A booklet on machine learning systems design with exercises
• cheatsheets VIP cheatsheets for Stanford's CS 229 Machine Learning https://stanford.edu/~shervine/teaching/cs-229/
• 101 NumPy Exercises for Data Analysis (Python)

Deep Learning

• deeplearningbook
  • Deep Learning Book Chinese Translation
  • DeepLearningBook读书笔记
• Dive into Deep Learning: an interactive deep learning book with code, math, and discussions, based on the NumPy interface. https://d2l.ai
  • 《动手学深度学习》：面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。 http://zh.d2l.ai
• Deep Learning for Java, Open-source, distributed, deep learning library for the JVM
• https://github.com/scutan90/DeepLearning-500-questions
• Metaflow is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.

Reinforcement Learning

• Awesome Deep Reinforcement Learning

Frameworks

• TensorFlow
  • stanford-tensorflow-tutorials This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research. http://cs20.stanford.edu
  • TensorFlow Course, Simple and ready-to-use tutorials for TensorFlow
  • EffectiveTensorflow TensorFlow 1.x and 2.x tutorials and best practices.
  • tinyflow, Tutorial code on how to build your own Deep Learning System in 2k Lines
  • tensorlayerDeep Learning and Reinforcement Learning Library for Scientists 🔥 http://tensorlayer.org
• Torch, A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT
  • PyTorch
    • PyTorch Tutorial for Deep Learning Researchers
    • pytorch-book, PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation
• Caffe is a deep learning framework made with expression, speed, and modularity in mind.
• scikit-learn: machine learning in Python https://scikit-learn.org

Game

• Awesome-Game-AI, A curated, but incomplete, list of game AI resources on multi-agent learning.
• RLCard: A Toolkit for Reinforcement Learning in Card Games
• while True: learn(), Simulator of a machine learning specialist
• DeepLeague - leveraging computer vision and deep learning on the League of Legends mini map + a dataset of over 100,000 labeled images to further A.I research within esports.
• Awesome StarCraft AI, A curated list of resources dedicated to StarCraft AI.
• PyGame Learning Environment (PLE) -- Reinforcement Learning Environment in Python.

AlphaZero

• AlphaZero_Gomoku, An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)
• Tencent/PhoenixGo, Go AI program which implements the AlphaGo Zero paper
• leela-zero Go engine with no human-provided knowledge, modeled after the AlphaGo Zero paper.

Datasets

• List_of_datasets_for_machine-learning_research
• The 50 Best Free Datasets for Machine Learning
• quickdraw-dataset, Documentation on how to access and use the Quick, Draw! Dataset.
• chinese-poetry The most comprehensive database of Chinese poetry 🧶最全中华古诗词数据库, 唐宋两朝近一万四千古诗人, 接近5.5万首唐诗加26万宋诗. 两宋时期1564位词人，21050首词。 http://shici.store
• Chinese-Word-Vectors 100+ Chinese Word Vectors 上百种预训练中文词向量