This book focuses on deep learning (DL), an important element of data science that involves predictive modeling. DL applications are widely used in fields such as finance, transportation, healthcare, the automotive industry, and advertising. The design of DL models is based on artificial neural networks and is influenced by the structure and function of the brain.

This book presents a comprehensive resource for those who wish to understand the techniques in deep learning. Key features include:

• Knowledge of the theory and design of modern deep learning models for real-world applications.
• Explanation of concepts and terminology in solving problems with deep learning.
• Exploration of the theoretical foundation for key algorithms and approaches in deep learning.
• Discussion of techniques for enhancing deep learning models.
• Identification of performance evaluation techniques for deep learning models.

Accordingly, the book covers the entire workflow of deep learning, providing awareness of each of the widely used models. It can be used as a guide for beginners, where the user can understand the relevant concepts and techniques. This book will be a useful resource for undergraduate and graduate students, engineers, and researchers starting to learn the subject of deep learning.

Pages: 184, Dimensions: 15.6x15.6cm