Reinforcement Learning and Deep RL Python Theory and Projects - DNN What Is Loss Function

The video tutorial covers activation functions in PyTorch, explaining how they can be implemented using Numpy or torch tensors. It delves into the concept of loss functions, describing how they are used to measure the performance of neural networks. The tutorial discusses the role of parameters in defining a neural network's architecture and performance. It explains different types of loss functions, such as squared loss and cross-entropy loss, and their importance in training. The video concludes with an overview of the training process, including gradient descent, and previews the next video on implementing loss functions in PyTorch.

Describe the process of calculating loss over all training examples.

What are some common types of loss functions used in neural networks?

How does gradient descent relate to the training of neural networks?