Practical Deep Learning for Coders: Lesson 1

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

in “Ask HN: What's the best lecture series you've ever seen?” · View on HN

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

in “Riffusion – Stable Diffusion fine-tuned to generate Music” · View on HN

> 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

in “I made a transformer by hand (no training)” · View on HN