This is the fifth article in the Machine Learning Series. It covers classic approaches to Hyperparameter Selection, including Bayesian Optimization, Gradient Optimization, Random Search, Multi-Arm Bandits and Neural Architecture Search.
This is the fourth article in the Machine Learning Series. It covers classic approaches to Robust Machine Learning, including Adversial Attacks, Adversial Training, Robust Features, Obfuscated Gradients and Provable Robust Certificates.
This is the first article in the Machine Learning Series. It covers the basics of optimization(GD,SGD,SVRG,Mirror Descent,Linear Coupling), generalization(No Free Lunch, PAC Learning, VC Dimension), and supervised learning(Linear Regression, Logistic Regression, Compressed Sensing).