TOC
My Bookshelf
Math
Introduction to Linear Algebra by Gilbert Strang
Linear Algebra and its applications by Gilbert Strang
- Difficulty: ⭐️⭐️
- Not only Gilbert Strang is an excellent researcher, he is perfect teacher.
- Overflowing diagrams across pages helps the understanding over Linear Algebra's different kinds of spaces.
- ML & DL is only briefly mentioned in the book but the required fundamental knowledge for ML & DL is easily described in the book.
Introduction to Probability by Joseph Blitzstein
- Difficulty: ⭐️⭐️
- Builds up materials from the basic and develops until the required knowledge just before MLE.
- It covers Probabilities, Random Variables, Probability Distributions, Transformations, Expectation, Markov Chains
Mathematics for Machine Learning by Deisenroth et al
- Difficulty: ⭐️⭐️⭐️
Computer Science
Code by Charles Petzold
- Difficulty: ⭐️
- The book is about history of code. Starts from the Morse Code and introduces how codes have developed over time.
Clean Code by Robert Martin
- Difficulty: ⭐️
ML & DL
Grokking Deep Learning by Andrew Trask
- Difficulty: ⭐️⭐️
- Easy book to begin with.
The Hundred-Page Machine Learning Book by Andriy Burkov
- Difficulty: ⭐️⭐️⭐️
Deep Learning with Python
- Difficulty: ⭐️⭐️⭐️
Hands-on Machine Learning with Scikit-learn and Tensorflow
- Difficulty: ⭐️⭐️⭐️
