Literally Everything About How Neural Networks Learn Explained Slowly (For Sleep)

Now streaming on Spotify https://open.spotify.com/show/033FVSz... How did neural networks go from a fringe scientific idea to the technology behind modern AI? This video traces the remarkable history of machine learning—from the early Perceptron and the AI winters to backpropagation, deep learning, GPUs, AlexNet, Transformers, GPT, and today’s large language models. We also look at why these systems became so powerful, how they actually learn, and the growing challenge of understanding what happens inside them. If you’ve ever wondered how decades of breakthroughs eventually led to modern AI, this is the story behind it. Resources: Nature — Learning Representations by Back-Propagating Errors https://www.nature.com/articles/323533a0 NeurIPS — ImageNet Classification with Deep Convolutional Neural Networks https://proceedings.neurips.cc/paper/... arXiv — Attention Is All You Need https://arxiv.org/abs/1706.03762 arXiv — Language Models are Few-Shot Learners (GPT-3) https://arxiv.org/abs/2005.14165