Reinforcement Learning and Deep RL Python Theory and Projects - DNN Why Activation Function Is Required Exercise Solutio

The video tutorial explores the concept of neural networks with linear activations and a single sigmoid-activated neuron in the last layer. It explains how such a structure collapses into a linear layer followed by a sigmoid, resembling logistic regression. The tutorial clarifies that logistic regression is not typically considered a neural network, despite its perceptron-like structure. It concludes that introducing non-linear activations in hidden layers transforms the model into a neural network.

What happens to the layers of a neural network if all hidden layers have linear activations?

How is logistic regression related to neural networks?

What is the significance of having a nonlinearity in the last layer of a neural network?

Why might logistic regression not be classified as a neural network?

What conditions must be met for a model to be considered a neural network?