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

The video tutorial explains the concept of neurons in neural networks, focusing on the importance of activation functions. It describes how neurons process inputs and the necessity of activation functions to prevent the network from acting like a single neuron. The tutorial also covers the architecture of neural networks, including input, hidden, and output layers, and explains the role of bias terms. It further discusses the computation of weights in hidden layers and introduces the concept of deep neural networks, characterized by having more than one hidden layer.

What happens if activation functions are not used in a neural network?

How are weights computed for neurons in a neural network?

What is the difference between a simple neural network and a deep neural network?