This is a collection of links, that I’ve found useful or just interesting in the past.
MIT Linear Algebra Course - I’ve found this a very good revisit of Linear Algebra course, especially the focus on interpretation of linear algebra with geometrical intuition and also the comparisons between normal and linear algebra which makes it a lot easier to understand Reinforcement Learning Course - Though, I still need to go through the majority of lectures, so far I fond it’s good introductory resource to Reinforcement Learning with an experienced teacher for free
Neural networks resources
Karpathy’s Stanford Course - Especially on the bit explaining the backpropagation algorithm I’ve found useful Overview of CNN layer - A good blog article about CNNs Durk Kingma’s Thesis - All the greatest developments of the last decade in one single thesis.
Bishop’s Pattern Recognition Book - A good, comprehensive book on ML techniques which is a safe place to go back for background information David Allen: Getting Things Done - one of the better productivity bibles Durrett’s Measure Theory book Speech ====== Simon King’s speech synthesis course