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Found 4 results for "neural networks"

Attention Mechanisms in LLMs: The Complete Guide to How Transformers Really Work

Learn how attention mechanisms power large language models (LLMs) like GPT-4 and Claude. This in-depth guide explains Query-Key-Value math, multi-head attention, and long-context processing with real code examples.

January 6, 202612 min read

#attention mechanisms#transformers#LLM

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🔮Machine Learning

Gradient Descent: The Workhorse of Machine Learning

Learn how gradient descent optimizes machine learning models by iteratively minimizing the loss function.

December 22, 202513 min read

#machine-learning#optimization#neural-networks

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🔮Vibe Coding

Why LLMs Can't Revise Tokens: The Feed-Forward Constraint Every Developer Must Understand

Learn why LLMs hallucinate and can't self-correct. Understand feed-forward token generation and master vibe coding strategies for better AI-assisted development.

January 6, 20269 min read

#LLM#token generation#feed-forward

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🎯Discrete Math

Graph Theory Basics: Nodes, Edges, and Paths

An introduction to graph theory covering fundamental concepts like vertices, edges, paths, and common graph algorithms.

January 4, 202611 min read

#graph-theory#discrete-math#algorithms