Listen to a Podcast About Deep Learning

Understand the neural network architectures powering modern AI — CNNs, RNNs, transformers, GANs, and diffusion models. Learn how deep learning enables image recognition, language generation, and more.

The modern deep learning revolution began in 2012 when AlexNet, a convolutional neural network designed by Alex Krizhevsky and colleagues at the University of Toronto, won the ImageNet competition by reducing the error rate from 26% to 16% — a margin so dramatic that it convinced the entire computer science community that neural networks had moved from niche curiosity to dominant paradigm. The 2017 paper "Attention Is All You Need" by Vaswani et al. at Google introduced the transformer architecture, which replaced recurrence with self-attention mechanisms and became the foundation for virtually all modern language models, including BERT (2018) and the GPT series that power today's chatbots and coding assistants. These architectures scale remarkably: GPT-3 used 175 billion parameters, while subsequent models have grown even larger, trained on trillions of tokens of text and requiring thousands of specialized GPUs running for months at a cost of tens of millions of dollars.

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