Fundamentals of Neural Networks - Lab 4 - Functional API

This tutorial explores the differences between functional and sequential APIs in designing neural networks. It begins with an introduction to both APIs, followed by a detailed explanation of how to use the sequential API. The tutorial then shifts focus to the functional API, highlighting its flexibility and advantages in designing complex architectures. The session concludes with a comparison of models created using both APIs, demonstrating the functional API's benefits in terms of flexibility and performance.

Why is it challenging to use sequential API for designing customized neural network architectures?

What are the main differences between functional API and sequential API in designing neural networks?

What is the significance of specifying the input dimensions when using functional API?

What changes would you need to make if the input data dimensions were different from 28 by 28?

Explain the process of defining a neural network architecture using functional API as described in the text.

Describe the role of the 'flatten' layer in the context of the neural network architecture discussed.

How does the functional API provide more flexibility compared to the sequential API?

What are the advantages of using a functional API for complex neural network architectures?

What is the importance of the output layer in a neural network architecture, particularly in the context of classification tasks?

How does adding an additional dense layer affect the performance of the neural network?