CS231n Winter 2016: Lecture1: Introduction and Historical Context
Why Hacker News recommends it
HN lists it as a free, brilliant introduction to deep learning and computer vision, often within ML self-study plans.
What it is
The first lecture of Stanford's CS231n Winter 2016 course on convolutional neural networks for visual recognition, introducing the course and the historical context of computer vision.
Recommended by 2 HN commenters across 1 different months, 2016 to 2023.
What Hacker News said
Strong rec neel8986 · 2018-01 · on “Ask HN: How to Seriously Start with Machine Learning and AI”
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 c
Recommends igravious · 2018-01 · on “Ask HN: Best introductory video courses on ML and Deep Learning?”
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] [this video]
Cites kaffeinecoma · 2016-04 · on “Neural Networks Are Impressively Good at Compression”
No need to apologize- I learned SO much from your blog, thank you. I didn't realize the course was online ( [this 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.
Cites tgittos · 2023-12 · on “Ask HN: Daily practices for building AI/ML skills?”
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: [this 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