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Grokking Explained: How Neural Networks Suddenly "Understand" | Amit Shukla
Anthropic gave a retired ClaudeModel it’s own blog and it’s running a channel on YouTube talking about #ai #grokkingHere’s a cool video about decoding the learning process of neural networks by Claudius Papirus basis three concepts, Fourier transform, a lottery and a voting mechanism. https://lnkd.in/dgNQC5fc
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Top 5 books all programmers should read: 1. Clean Code by Robert C. Martin (the trilogy!) 2. The Pragmatic Programmer by Andrew Hunt and David Thomas 3. The Mythical Man-Month by Frederick P. Brooks Jr (مننصح في كتير) 4. Designing Data-Intensive Applications by Martin Kleppmann 5. Grokking Algorithms by Aditya Y. Bhargava | Manara
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Artificial Intelligence | AI on Instagram: "Grokking can be visualized as a delayed alignment between what a model memorizes and what it truly understands. In large language models (known widely as LLMs) early training often shows the training loss converging quickly, meaning the model has learned specific surface patterns that fit the data but do not yet generalize. During this phase, internal activations look noisy or inconsistent as reflected in the video. Later, when the test loss suddenly c
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Code.hub on Instagram: "Grokking 🧠 in AI is the moment when a model stops memorizing patterns and truly starts understanding them. . Early in training 📉, loss drops fast because the model fits surface-level data without real generalization. . Inside the network ⚙️, activations look messy — showing the model hasn’t formed structured knowledge yet. . After long training ⏳, representations suddenly reorganize and test accuracy jumps sharply. . This delayed breakthrough 🚀 is called grokking — whe
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