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

The video tutorial explains the significance of the bias term in neural networks, detailing how it allows hyperplanes to not pass through the origin, which can be crucial for accurate boundary representation. It also discusses conventions for counting layers in neural networks, emphasizing the difference between counting hidden layers and including the output layer. The architecture of fully connected feedforward neural networks is described, highlighting the role of bias and the arrangement of units. Finally, the video introduces the concept of training neural networks using datasets to find optimal weights.

How do different authors define the total number of 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 can one train a neural network given a dataset?