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15min chapter
Why Machines Learn: The Math Behind AI
Breaking Math Podcast
The Evolution of Backpropagation and Neural Networks
This chapter traces the historical development of backpropagation, starting from Frank Rosenblatt's early work to its major implementation in deep neural networks in 1986. It discusses the rise of neural networks, emphasizing breakthroughs like AlexNet and the significance of large datasets and GPUs in their success. The chapter also explores the philosophical implications of machine consciousness and the vital role of fundamental mathematical concepts in understanding and advancing artificial intelligence.
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