What is backpropagation really doing? | Chapter 3, Deep learning

In this monologue podcast, Grant Sanderson explains backpropagation, the core algorithm behind neural networks. He begins with a recap and an intuitive walkthrough of the algorithm, avoiding formulas initially, and mentions a follow-up video will delve into the calculus. Sanderson reviews neural networks, gradient descent, and cost functions, emphasizing the sensitivity of the cost function to weights and biases. He breaks down the backpropagation algorithm, focusing on how each training example affects weight and bias adjustments, using the example of recognizing handwritten digits. The explanation covers how to adjust weights and biases, the influence of neuron brightness, and the concept of propagating adjustments backward through the network. Sanderson also touches on stochastic gradient descent as a computationally efficient alternative and summarizes the key steps of backpropagation, its practical implementation using mini-batches, and the importance of labeled training data.