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Practical Deep Learning for Coders: Lesson 1

Name: Practical Deep Learning for Coders: Lesson 1
Description: 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.

Jeremy Howard

3 videos recommended on HN

YouTube

2 HN users recommended this course2 months2022–2023 score 2.5

Years with an HN recommendation, 2007–2026

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

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.

“The lectures are super hands on - Jeremy actually fires up a jupyter notebook and runs code which often surprises him” (HN, 2023)

1 strong recommendations · 1 recommendations · 1 citations · Courses · intro · partly dated

Machine learning & deep learning Computer vision, speech & NLP University lectures & courses Courses AI, Machine Learning & Data

3 Hacker News comments

From the HN archive; up to 20 kept per video, favoring different commenters. Recommendations first.

@ujjwalgrover Jan 2023 Strongly recommends

on HN thread 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]

Hands-on lectures run in Jupyter, with Kaggle deep dives.

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@superpope99 Dec 2022 Recommends

on HN thread 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]

First lesson covers image techniques applied to audio spectrograms.

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Citations, criticism and jokes

@elesiuta Sep 2023 Cites it

on HN thread 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]

Shows pre-deep-learning hand-tuned image recognition.

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