Machine learning (ML) is a set of techniques that allow computers to learn from data and experience, rather than requiring humans to specify the desired behaviour manually.
Advances in generative modeling and adversarial learning have given rise to renewed interest in differentiable two-players games, with much of the attention falling on generative adversarial networks (GANs).

Sign up This repository contains all the material of the session "Understanding machine learning from theory to algorithms"

First, we discuss the issue of gradient explosion/vanishing and the more general issue of undesirable spectrum, and then discuss practical solutions including careful initialization and normalization methods. Detailed tutorial on Deep Learning & Parameter Tuning with MXnet, H2o Package in R to improve your understanding of Machine Learning. I focus on machine learning theory and applied probability, and also have broad interests in theoretical computer science and related math.

Solving these games introduces distinct challenges …

Course Objective. Machine Learning Path Recommendations. ML has become increasingly central both in AI as an academic eld, and in industry.

To become an expert in machine learning, you first need a strong foundation in four learning areas: coding, math, ML theory, and how to build your own ML project from start to finish.

Understanding Machine Learning: From Theory to Algorithms. Chapter notes I made while studying for CS5339: Machine Learning Theory & Algorithms. Understanding Machine Learning: From Theory to Algorithms by Shai Ben-David and Shai Shalev-Shwartz; Some of these books are freely available on the Internet. P. Liang course notes.

Machine learning (ML) is a set of techniques that allow computers to learn from data and experience, rather than requiring humans to specify the desired behaviour manually. O. Bousquet, S. Boucheron and G. Lugosi Introduction to Statistical Learning Theory (Tutorial). - tim-hub/machine-learning-books

Additionally, I completed a few courses as given below.
Kernel Methods for Pattern Analysis . In the summers of 2020, I completed one career track, named Machine Learning Scientist with Python on DataCamp. S. Shalev-Shwartz and S. Ben-David Understanding Machine Learning: From Theory to Algorithms (Online Book). Although machine learning (and deep learning in particular) has made great advances in recent years, our mathematical understanding of it is shallow.