Dive into Deep Learning

An interactive deep learning book with code, math, and discussions, based on the NumPy interface by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola. With 900 pages, this seems to be one of the most comprehensive one-stop resources that goes from Linear Neural Networks and Multilayer Perceptrons all the way to modern Deep Learning architectures including Attention Mechanisms and Optimization Algorithms – giving you all three: Theory, Math & Code.


Math for Machine Learning

Note: We have bi-weekly remote reading sessions goingthrough all chapters of the book. If you'd like to join check out this blog post and join us on Meetup.

Part I: Mathematical Foundations

  1. Introduction and Motivation
  2. Linear Algebra
  3. Analytic Geometry
  4. Matrix Decompositions
  5. Vector Calculus
  6. Probability and Distribution
  7. Continuous Optimization

Part II: Central Machine Learning Problems

  1. When Models Meet Data
  2. Linear Regression
  3. Dimensionality Reduction with Principal Component Analysis
  4. Density Estimation with Gaussian Mixture Models
  5. Classification with Support Vector Machines


Interactive tools

Seeing Theory: Probability and Stats

A visual introduction to probability and statistics.


Video lectures


3blue1brown, by Grant Sanderson, is some combination of math and entertainment, depending on your disposition. The goal is for explanations to be driven by animations and for difficult problems to be made simple with changes in perspective.


Recommended video series:

The classic: Gilbert Strang MIT lectures on Linear Algebra


Online courses

Essential Math for Machine Learning: Python Edition

  • Equations, Functions, and Graphs
  • Differentiation and Optimization
  • Vectors and Matrices
  • Statistics and Probability