Intel
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Anatomize Deep Learning with Information Theory
Naftali Tishby's information bottleneck applied to deep learning, using information theory to describe how a network's representations grow and transform over the course of training.
Why it mattersIt unpacks Tishby's Information Bottleneck framework and the two-phase (fitting then compression) view of DNN training, giving practitioners an information-theoretic lens on generalization that most engineering-focused writeups skip.
From GAN to WGAN
A walk from the original generative adversarial network to Wasserstein GAN.
Why it mattersA rigorous, well-illustrated walkthrough of why vanilla GANs are unstable and how Wasserstein distance fixes the gradient/convergence problems — useful grounding for anyone building or debugging generative models.
Attention Is All You Need
The 2017 paper that dropped recurrence and convolution for self-attention alone, introducing the Transformer — the architecture every large language model still builds on.
Why it mattersEvery model you use descends from this architecture, and reading it is how the rest of the stack stops being magic.
An index of the vibe-coding frontier. Corrections welcome.