CS231n Winter 2016: Lecture1: Introduction and Historical Context

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 ( [VIDEO] ) 2) NLP ( [LINK] ) The Vision course by Karparthy can be a very good introduction to Deep learning. Also, the mother book for deep learning ( [LINK] )is good

fast.ai Making neural nets uncool again. fast.ai is dedicated to making the power of deep learning accessible to all.[0] Udacity. Intro to Machine Learning: Pattern Recognition for Fun and Profit[1] MIT 6.S099: Artificial General Intelligence[2] Lecture Series on Artificial Intelligence by Prof. P. Dasgupta, Department of Computer Science & Engineering, Indian Institute of Technology, Kharagpur[3] DeepMind. Reinforcement Learning Course by David Silver[4] Stanford Winter Quarter 2016 class: CS231n: Convolutional Neural Networks for Visual Recognition.[5] [0] [LINK] [1] [LINK] [2] [LINK] [3] [LINK] [4] [LINK] [5] [VIDEO]

No need to apologize- I learned SO much from your blog, thank you. I didn't realize the course was online ( [VIDEO] ). Also, looks like there's a subreddit for it as well: [LINK] It's really wonderful that all of this is freely available, thank you.

This is exactly the boat I'm in. I have a 1hr train commute to work that I spend skilling up in AI. I've been following the space for about 15 years and have done a bunch of self learning of earlier ML techniques (the early Stanford ML MooCs) so I'm not coming in cold. What I'm doing is: - Following along with Karpathy's videos, which has been mentioned: [LINK] - About to follow along with CS 231n, also mentioned: [VIDEO] - Trying ideas and theories in a Jupyter notebook - Reading papers I would agree with other commenters that recommend learning how to implement a paper. As someone who barely managed to get their undergraduate degree, papers are intimidating. I don't know half the terms and the equations, while short, look complex. Often it will take me several reads to understand the gist, and I've yet to successfully implement a paper by myself without checking other sources. But I also know that this is where the tech is ultimately coming from and that any hope of staying current outside of academia is dependent on how well I can follow papers. I've been doing this for about a month now, and I feel I definitely understand more of the theory of how most of this stuff works and can train a simple attention based model on a small-ish amount of data. I don't feel I could charge someone money for my skills yet, but I do feel that I will feel ready with about 6 months - 1 year of doing this.