Reinforcement Learning and Deep RL Python Theory and Projects - Perceptron

The video tutorial introduces deep neural networks, focusing on perceptrons, which are fundamental units in these networks. It explains the role of inputs, weights, and the importance of activation functions. The tutorial also covers how neurons are connected to form larger networks and provides a practical implementation of perceptrons in PyTorch, initially without activation functions.

What is the purpose of connecting multiple neurons together in a network?

Why is it important to have a bias term in a neural network?

How do you implement a neuron without an activation function in Pytorch?