Theoretical & Applied Mathematics for Deep Learning, Machine Learning and AI
Comprehensive books are always better than short-term online courses. (Required) Algebra: Linear Algebra Done Right, S. Axler Matrix Analysis and Applied Linear Algebra, Carl D. Meyer Linear Algebra & Its Applications, David C. Lay Probability: A first course in Probability, Sheldon Ross Probability & Random Processes, Grimmett & Stirzaker A User's Guide to Measure Theoretic Probability, David Pollard Statistics: All of Statistics, L Wasserman Elements of Large Sample Theory, Lehmann An Introduction to Generalised Linear Models, Dobson Generalised Linear Models, McCullagh & Nelder Statistical Inference, Casella & Berger Monte Carlo Statistical Methods, Robert & Casella Bayesian Data Analysis, Gelman Numerical Optimisation: Numerical Optimisation, Nocedal & Wright Introduction to Linear Optimisation, Bertsimas Convex Optimisation, Boyd Practical Optimisation, Antoniou & Lu Practical Optimisation, Gill & Murray ...