Summary

In this chapter, we explored supervised deep learning, including the types of problems it can be used to solve and the techniques for implementing and training DL models. Supervised deep learning involves training a model on labeled data to make predictions on new data. We also covered a variety of supervised learning use cases on different problem types, including binary classification, multiclassification, regression, and multitask and representation learning. The chapter also covered techniques for training DL models effectively, including regularization and hyperparameter tuning, and provided practical implementations in the Python programming language using popular DL frameworks.

Supervised deep learning can be used for a wide range of real-world applications in tasks such as image classification, natural language processing (NLP), and speech recognition. With the knowledge provided in this chapter, you should be able to identify supervised learning applications and...