In this chapter, you will be covering a few prelinaries that will help you develop basic skills needed to get started with deep learning. Some of these important preliminaries include data manipulation, data processing, linear algebra, calculus, automatic differentiation, and probability.
- The Matrix Calculus You Need for Deep Learning (by Terence Parr and Jeremy Howard)
- Deep Learning - Linear Algebra (by Ian Goodfellow and Yoshua Bengio and Aaron Courville)
- Deep Learning - Numerical Computation (by Ian Goodfellow and Yoshua Bengio and Aaron Courville)
- MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 (by MIT, Gilbert Strang)
- Basic Calculus (by Steve Butler)
- Mathematics for Machine Learning - Multivariate Calculus (by Imperial College London)
- Mathematics for Machine Learning - Linear Algebra (by Imperial College London)
- Gilbert Strang lectures on Linear Algebra (MIT) (by Gilbert Strang)
- Dive into Deep Learning - Preliminaries (by Aston Zhang, Zack C. Lipton, Mu Li, and Alex J. Smola)
- Dive into Deep Learning - Mathematics for Deep Learning (by Aston Zhang, Zack C. Lipton, Mu Li, and Alex J. Smola)
- Mathematics for Machine Learning (by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong)
- Neural Networks and Deep Learning (by Michael Nielsen)
- Deep Learning (by Ian Goodfellow and Yoshua Bengio and Aaron Courville)
- Dive into Deep Learning (by Aston Zhang, Zack C. Lipton, Mu Li, and Alex J. Smola)
- Deep Learning - Probability and Information Theory (by Ian Goodfellow and Yoshua Bengio and Aaron Courville)
- Statistics and Probability (by Khan Academy)
- Discrete Math (Full Course: Sets, Logic, Proofs, Probability, Graph Theory, etc) (by Trefor Bazett)
- Statistics and Probability (by Great Learning)
- Dive into Deep Learning - Mathematics for Deep Learning (by Aston Zhang, Zack C. Lipton, Mu Li, and Alex J. Smola)
- Elements of Statistics (Trevor Hastie, Robert Tibshirani, Jerome Friedman)
- TBA