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

The video tutorial explains the importance of the bias term in neural networks, discussing how it allows hyperplanes to not pass through the origin, which can be crucial for achieving the correct decision boundary. It also covers conventions for counting layers in neural networks, highlighting the difference between counting only hidden layers versus including the output layer. The architecture of fully connected neural networks is described, emphasizing the role of bias and connections between layers. The video concludes with a preview of the next topic: training neural networks.

How do different authors count the layers in a neural network?

What is the significance of the bias term in a neural network?

Explain the concept of a hyperplane in the context of neural networks.

What happens to the hyperplane if the bias term is not included?

Describe the architecture of a fully connected feedforward neural network.

What are hyperparameters in the context of deep neural networks?

How do you train a neural network given a dataset?