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    Practical Deep Learning for Coders: Lesson 1

    Jeremy Howard

    2 HN recommenders · recommended in 3 months, 2022–2023

    YouTube

    1:22:56 · 611K views · Jul 21, 2022

    Lesson 1 of fast.ai's Practical Deep Learning for Coders, taught by Jeremy Howard. It takes coders from running code in a Jupyter notebook straight into training a real image model.

    HN considers the course one of the best hands-on ways into deep learning, praising the code-first teaching style.

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    3 comments from Hacker News

    1. @ujjwalgroverJan 2023· in “Ask HN: What's the best lecture series you've ever seen?”

      Practical deep learning for coders (2022 edition) By Jeremy, who is the founding researcher at fast.ai. Three things I love about this series 1. Jeremy seems like a power user of Jupyter notebook and uses them beautifully to run the lectures. The book on fastai is also written in Jupyter notebooks. 2. The lectures are super hands on - Jeremy actually fires up a jupyter notebook and runs code which often surprises him 3. I love how he describes he deep dives into a specific Kaggle competition. Describes in great detail his own attempts at getting up the leaderboard. It's almost like watching a poker player reveal her decision process before they make a move. this video

      Strongly recommendsView on HN
    2. @superpope99Dec 2022· in “Riffusion – Stable Diffusion fine-tuned to generate Music”

      I'm super excited about the Audio AI space, as it seems permanently a few years behind image stuff - so I think we're going to see a lot more of this. If you're interested, the idea of applying Image processing techniques to Spectrograms of audio is explored in brief in the first lesson of one of the most recommended AI courses on HN: Practical Deep Learning for Coders this video

      RecommendsView on HN
    3. @elesiutaSep 2023· in “I made a transformer by hand (no training)”

      > weights of a model, that a domain expert can tweak by hand This sounds similar to how image recognition was done before deep learning [1] [1] this video

      CitesView on HN