How Neural Networks Learn: From Random Numbers to Text Prediction скачать в хорошем качестве

How Neural Networks Learn: From Random Numbers to Text Prediction

Ever wondered how an LLM goes from billions of random numbers to predicting coherent text? In this video, we break down the training process that transforms neural networks into powerful language models—and why this requires entire data centers of specialized hardware. You'll learn why prediction is really about calculating 100,277 probabilities simultaneously, how tiny parameter adjustments across trillions of attempts create emergent intelligence, and what actually happens inside a Transformer architecture. 📚 Key concepts covered: • Prediction as probability distribution over all possible tokens • The training loop: predict → compare → adjust → repeat billions of times • Context windows and their computational constraints • Transformer architecture: embeddings, attention blocks, and matrix multiplications • Why neural networks are stateless mathematical functions, not biological neurons • GPU parallelism and why training requires data center scale ───────────────────────────── 🎓 ORIGINAL SOURCE This video distills concepts from: "How LLMs Work" by Anthropic    • Deep Dive into LLMs like ChatGPT   Full credit to the original creators. Please watch the complete lecture for deeper understanding. ───────────────────────────── 📖 About Lecture Distilled Long lectures. Short videos. Core insights. We transform hour-long technical lectures into focused concept videos that respect your time while preserving the essential knowledge. 🔗 GitHub: https://github.com/Augustinus12835/au... ───────────────────────────── #NeuralNetworks #MachineLearning #LLM #Transformers #DeepLearning #AIExplained #GPUComputing #NLP