Neural Network Architectures

The major neural network architecture families beyond transformers, including CNNs, RNNs, GANs, VAEs, and graph networks.

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Neural Network Basics for LLMs

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Neural networks are computational systems that learn patterns from data by adjusting internal parameters called weights across interconnected layers. For large language models, the key building blocks include layers that transform text into numerical representations, backpropagation that propagates errors backward to update weights, gradient descent that optimizes learning, and activation functions that introduce non-linear decision boundaries. Also known as: NN Fundamentals.

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Embeddings & Vector Search 1 topics Inference Optimization 1 topics LLM Training & Pre-Training 1 topics Model Evaluation & Benchmarks 1 topics Safety & Red Teaming 1 topics Transformer & Attention Internals 1 topics