THE VIDEO SHELF · computer vision deep learning
Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition
The first lecture of Stanford's CS231n course on convolutional neural networks for visual recognition. It surveys the history and key challenges of computer vision, from the Cambrian explosion and Hubel and Wiesel to ImageNet.
Why HN readers recommended it
HN recommends the CS231n lectures as a solid, applied deep learning course for self-study.
This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.
Video & recommendation details
- Creator / channel
- Stanford University School of Engineering
- Topic
- computer vision deep learning
- Format
- lecture
- Level
- intro
- HN recommenders in dataset
- 2
- HN mentions in dataset
- 3
- Recommendation years in dataset
- 2018–2025
These counts and years describe the included Hacker News records; they are not YouTube views or the video’s publication date.
From the original HN conversations
Excerpts from the recorded comments. Follow each source for the full comment and surrounding discussion.
The Stanford course is pretty good. [VIDEO] For something quicker & less applied, maybe the 3b1b series on neural networks: [LINK]
dahart on Hacker News · 2018-04
In Microsoft makes AI training courses available to the publicFor studying? Mainly watching and re-watching Karpathy's 'Zero To Hero'[1] and Stanford's 'Introduction to Convolutional Neural Networks for Visual Recognition'[2], also a lot of transformers from scratch videos like Umar Jamali's videos[3], and I also study backwards to McCulloch and Pitts. Reading the 30 papers …
jackdoe on Hacker News · 2025-06
In The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.