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Fast.ai - Practical Deep Learning
Nathan Wailes - Blog - GitHub - LinkedIn - Patreon - Reddit - Stack Overflow - Twitter - YouTube
Fast.ai - Practical Deep Learning
- 2 Part 1
- 3 Part 2
- 3.1 Stable Diffusion
- 3.2 Diving Deeper
- 3.3 Matrix multiplication
- 3.4 Mean shift clustering
- 3.5 Backpropagation & MLP
- 3.6 Backpropagation
- 3.7 Autoencoders
- 3.8 The Learner framework
- 3.9 Initialization/normalization
- 3.10 Accelerated SGD & ResNets
- 3.11 DDPM and Dropout
- 3.12 Mixed Precision
- 3.13 DDIM
- 3.14 Karras et al (2022)
- 3.15 Super-resolution
- 3.16 Attention & transformers
- 3.17 Latent diffusion
Practical Deep Learning for Coders - Practical Deep Learning
My understanding is that this is the single-best course on deep learning out there. Jeremy Howard is a great teacher; unlike a lot of other people teaching this stuff, he does a great job of explaining potentially-complicated ideas in simple wa