🎬AI Lectures7

This lecture kicks off Stanford CS230 and explains what deep learning is: a kind of machine learning that uses multi-layer neural networks to learn complex patterns. Andrew Ng highlights its strength in understanding images, language, and speech by learning layered features like edges, textures, and objects. The message is that deep learning’s power comes from big data, flexible architectures, and non-linear functions that let models represent complex relationships.

This session teaches two essentials for building language models: PyTorch basics and resource accounting. PyTorch is a library for working with tensors (multi‑dimensional arrays) and can run on CPU or GPU. You learn how to create tensors, perform math (including matrix multiplies), reshape, index/slice, and use automatic differentiation to compute gradients for training.

GPUs (Graphics Processing Units) are critical for deep learning because they run thousands of simple math operations at the same time. Language models like Transformers rely on huge numbers of matrix multiplications, which are perfect for parallel processing. CPUs have a few strong cores for complex, step-by-step tasks, while GPUs have many simpler cores for doing lots of math in parallel. Using GPUs correctly can make training and inference dramatically faster.

This lesson teaches two big ways to train neural networks on many GPUs: data parallelism and model parallelism. Data parallelism copies the whole model to every GPU and splits the dataset into equal shards, then averages gradients to take one update step. Model parallelism splits the model itself across GPUs and passes activations forward and gradients backward between them.

This session explains how to speed up and scale training when one GPU or a simple setup is not enough. It reviews data parallelism (split data across devices) and pipeline parallelism (split model across devices), then dives into practical fixes for their main bottlenecks. The key tools are gradient accumulation, virtual batch size, and interleaved pipeline stages. You’ll learn the trade‑offs between memory use, communication overhead, and idle time.