Data Science and Machine Learning (Theory and Projects) A to Z - Deep Neural Networks and Deep Learning Basics: Training

The video introduces the concept of backpropagation in neural networks using a binary classification example. It explains the forward and backward pass processes, where the forward pass involves computing the loss with current parameters, and the backward pass updates parameters to minimize the loss. The video also touches on stopping criteria and technical issues in training, such as learning rates and network architecture. A preview of the next video is provided, which will cover more technical details like learning rates, stopping conditions, and network architecture.

What is the purpose of the back propagation process in neural networks?

Explain the difference between the forward pass and the backward pass in the training process.

Discuss the importance of updating parameters during the training of a neural network.

What are some of the technical issues that need to be addressed in the training process of neural networks?

What factors should be considered when determining the learning rate for a neural network?