Deep Learning - Crash Course 2023 - Keras Basics

The video tutorial explains how to build a neural network using Keras. It covers adding layers, specifying units, and using activation functions like sigmoid. The process involves creating a sequential model, compiling it with an optimizer, loss function, and metrics, and fitting it with training and validation data. The tutorial emphasizes the importance of defining input shapes and configuring the learning process. It concludes with a summary of the five-step process: data preparation, model definition, learning configuration, and model fitting.

What is the purpose of adding layers in a neural network?

• To increase the complexity and learning capacity of the model

• To reduce the number of parameters

• To decrease the computational cost

• To simplify the model structure

What does a sequential model in Keras represent?

• A model with only one layer

• A linear stack of layers

• A model with parallel layers

• A model with no layers

Which of the following is NOT a component of the compile method?

• Loss function

• Metrics

• Optimizer

• Activation function

Why is validation data used during the training process?

• To simplify the model architecture

• To reduce the size of the training data

• To increase the training speed

• To visualize model performance on unseen data

What is the first step in the five-step process of training a neural network?

• Performing the fit

• Configuring the learning process

• Preparing the data

• Defining the model