Reinforcement Learning and Deep RL Python Theory and Projects - DNN Implementation Stochastic Gradient Descent

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.

What are the key components needed to define a training function for a neural network?

How can the average loss be calculated after each epoch?

Why is it important to shuffle the data before each epoch?

What is the difference between stochastic gradient descent and batch gradient descent?