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Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition

Stanford University School of Engineering · lecture · Watch on YouTube

Machine learning & deep learningComputer vision, speech & NLPUniversity lectures & courses

What it is

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 Hacker News recommends it

HN recommends the CS231n lectures as a solid, applied deep learning course for self-study.

  • 2 people recommended it
  • 2 different months
  • 0 strong recommendations
  • first 2018, last 2025

What HN commenters said

  1. “The Stanford course is pretty good.”

    Stanford CNN course lectures, a solid applied deep learning course

    dahart, 2018-04 · Recommendation · in “Microsoft makes AI training courses available to the public”

  2. “Mainly watching and re-watching Karpathy's 'Zero To Hero'[1] and Stanford's 'Introduction to Convolutional Neural Networks for Visual Recognition'[2]”

    Part of the commenter's study regimen for learning neural networks

    jackdoe, 2025-06 · Recommendation · in “The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]”

Excerpts from Hacker News comments, linked to the originals. Summaries and labels were written by a language model from those comments.

Topics: cs231n, convolutional neural networks, computer vision, stanford, imagenet

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