This video tutorial covers the implementation of a training function for stochastic gradient descent (SGD) in a neural network. It begins with an introduction to gradient descent, followed by setting up the training function with input dimensions and a loss function. The main focus is on writing the SGD training loop, handling errors, and debugging common issues. The video also delves into the technical details of SGD, discussing both theoretical and practical aspects. It concludes with a summary and a brief introduction to batch gradient descent.

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