Best of Deep Learning — November 2025

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    Article
    Avatar of sebastianraschkaSebastian Raschka·28w

    Recommendations for Getting the Most Out of a Technical Book

    A structured five-step approach to learning from technical books: start with an offline read-through to grasp the big picture, follow with hands-on coding by retyping examples, complete exercises to solidify understanding, review notes and explore additional resources, and finally apply concepts in personal projects. The method emphasizes focused reading sessions, active engagement with code, and practical application over passive consumption.

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    Article
    Avatar of tdsTowards Data Science·29w

    We Didn’t Invent Attention — We Just Rediscovered It

    Attention mechanisms in AI transformers aren't novel inventions but rediscoveries of fundamental optimization principles. The same mathematical pattern—selective amplification combined with normalization—emerges independently across evolution (500+ million years of neural systems), chemistry (autocatalytic reactions), and AI (gradient descent). This convergence suggests attention represents a universal solution to information processing under energy constraints. Reframing attention as amplification rather than selection offers practical insights for improving AI architectures: decoupling amplification from normalization, exploring non-content-based amplification, implementing local normalization pools, and designing systems that operate at critical dynamics for optimal information processing.

  3. 3
    Article
    Avatar of sebastianraschkaSebastian Raschka·29w

    Beyond Standard LLMs

    Explores alternatives to standard autoregressive transformer LLMs, including linear attention hybrids like Qwen3-Next and Kimi Linear that use Gated DeltaNet for improved efficiency, text diffusion models that generate tokens in parallel through iterative denoising, code world models that simulate program execution for better code understanding, and small recursive transformers like TRM that refine answers through iterative self-refinement. While traditional transformer LLMs remain state-of-the-art, these alternatives offer promising trade-offs between efficiency and performance for specific use cases.

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