Here are some resources about machine learning I collected during learning.
- A Tour of Machine Learning Algorithms by Jason Brownlee
- UC Berkeley CS188 Intro to AI -- Course Materials
- Tutorial Slides by Andrew Moore(detailed)
- Stanford CS229: Machine Learning
- Understanding the bias-Variance Tradeoff
- sklearn ML algorithm cheat-sheet
- feature selection 1 2 3 by Jason Brownlee
- Random Search for Hyper-Parameter Optimization
- xgboost
- Visualizing Gradient Boosting
- 数学之美番外篇:平凡而又神奇的贝叶斯方法 by 刘未鹏
- SVM原理及解释/Lagrange Multiplier and KKT
- t-SNE (t-Distributed Stochastic Neighbor Embedding)
- tensorflow playground
- DL of class central
- The neural network zoo
- Machine Learning Notebook(DL)
- diveintodeeplearning
- tensorfly
- Understanding LSTM Networks by Christopher Olah
- An Intuitive Explanation of Convolutional Neural Networks
- A guide to convolution arithmetic for deep learning
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting
- optimization
- MIT deep learning book
- Neural Networks and Deep Learning by Michael Nielsen
- deeplearning.ai by Andrew Ng. (course notes by 黄海广)
- National Taiwan Univ
- Stanford CS20: Tensorflow for Deep Learning Research
- Stanford CS231n: Convolutional Neural Networks for Visual Recognition
- A Full Hardware Guide to Deep Learning by Tim Dettmers
- CV datasets