Digest and Test: Deep Learning Basics and Environment Test

In this chapter, we covered essential knowledge for building and training deep learning models, starting with a simple example based on linear regression. We covered important topics in machine learning, such as parameter estimation and backpropagation, loss functions, and diverse neural network layers. We described how to set up a deep-learning programming environment that will be used throughout this course. After installing our deep-learning programming environment, we trained and evaluated a simple neural network model for the classification of handwritten digits.

Test your understanding

Put your knowledge to the test with a quiz designed to reinforce your understanding.