Lecture 1 | Natural Language Processing with Deep Learning

This is kind of a masters degree course i created for myself to get knowledge of Machine Learning from bottoms up First, you need a strong mathematical base. Otherwise, you can copy paste an algorithm or use an API but you will not get any idea of what is happening inside Following concepts are very essential 1) Linear Algebra (MIT [LINK] ) 2) Probability (Harvard [LINK] ) Get some basic grasp of machine learning. Get a good intuition of basic concepts 1) Andrew Ng coursera course ( [LINK] ) 2) Tom Mitchell book ( [LINK] ) Both the above course and book are super easy to follow. You will get a good idea of basic concepts but they lack in depth. Now you should move to more intense books and courses You can get more in-depth knowledge of Machine learning from following sources 1)Nando machine learning course ( [LINK] ) 2)Bishops book ( [LINK] ) Especially Bishops book is really deep and covers almost all basic concepts. Now for recent advances in Deep learning. I will suggest two brilliant courses from Stanford 1) Vision ( [LINK] ) 2) NLP ( [VIDEO] ) The Vision course by Karparthy can be a very good introduction to Deep learning. Also, the mother book for deep learning ( [LINK] )is good

For those who like Prof Manning's style and want to get more in depth, here are the lecture videos for the NLP course he teaches at Stanford: [VIDEO]

I have read like 1/3 of the comments but its interesting that the most interesting problem of everything we are talking about was only noticed by a non-programmer (so far). Everything that was talked about in the comments all hinge on the state of the art (SOTA) of ai in natural language processing (nlp). John Carmack recommended [VIDEO] stanford's nlp class (i didnt know who he was until i saw that post on hn). That class is good. it should take a long time to grok. you'll be solid up to LSTMs a general current standard.