Can’t go wrong with Maths…

Linear Algebra

• 💖✨Introductory Linear Algebra for Machine Learning https://pabloinsente.github.io/intro-linear-algebra
• ✨Additional Resources: Intro to Linear Algebra Linear Algebra II: Matrix Operations

Calculus

• Essential Overview: https://ml-cheatsheet.readthedocs.io/en/latest/calculus.html
• Hands-on Resources: Calculus I: Limits & Derivatives Calculus II: Partial Derivatives & Integrals

Probability And Statistics

• Probability & Information Theory: Comprehensive notebook covering fundamental concepts in probability and information theory essential for machine learning.
• Intro to Statistics: In-depth introduction to statistical concepts and their applications in machine learning and data analysis.

For TensorFlow & PyTorch

• The D2L (Dive into Deep Learning) book covers all essential mathematical preliminaries: https://d2l.ai/chapter_preliminaries/index.html