Techniques & Methods
Backpropagation
Backpropagation computes gradients of the loss function with respect to all model parameters using the chain rule of calculus, then uses these gradients to update weights via gradient descent. It is the core algorithm that makes training deep neural networks feasible.
Efficient backpropagation through billions of parameters in modern LLMs requires specialized hardware (GPUs/TPUs), automatic differentiation libraries (PyTorch, JAX), and techniques like mixed-precision training and gradient checkpointing to manage memory.
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Related Terms
Core Concepts
Deep Learning
Subset of ML using neural networks with many layers to analyze complex data representations.
Model Components
Neural Network
Computational system of interconnected nodes inspired by the human brain that learns to recognize patterns.
Techniques & Methods
Training
Teaching a model to make accurate predictions by exposing it to large datasets.