Deep Learning CNN Convolutional Neural Networks with Python - BiasTerm

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 the conventions for counting layers in neural networks, emphasizing the difference between counting hidden layers and including the output layer. The architecture of neural networks is described, highlighting the role of bias and the arrangement of units. The video concludes with an introduction to training neural networks using datasets, setting the stage for the next tutorial.

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 for a classification problem?