Fundamentals of Neural Networks - Lab 5 - Building Deeper and Wider Model

This lab session covers building a wider and deeper neural network architecture using the functional API. It contrasts the functional API with the sequential API, highlighting the flexibility of the former in creating complex architectures. The session details the definition of input paths, hidden layers, and the use of the Raylu activation function. It explains the convergence of paths and the handling of multiple outputs, emphasizing the importance of matching dimensions in the training set. The session concludes with implementing the architecture using class objects in Python for cleaner code.

What new insight or understanding did you gain from this video?

Evaluate responses using AI:

OFF