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Practical Deep Learning for Coders: Lesson 1

Jeremy Howard · 611.4K views · 2022-07-21 · 1:22:56

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Overview

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.

On Hacker News

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

Recommended by 2 HN people across 2 months · HN mentions 2022–2023

Topics

Machine learning & deep learningComputer vision, speech & NLPUniversity lectures & courses
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fast.aijeremy howardpytorchjupyterimage classification

What people said

ujjwalgrover · 2023-01 · Strong recommendation

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. [VIDEO]

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superpope99 · 2022-12 · Recommendation

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 [VIDEO]

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elesiuta · 2023-09 · Reference

> 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] [VIDEO]

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Sources

Video by Jeremy Howard, played through YouTube.

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