Description
Book SynopsisThis book focuses on deep learning (DL), which is an important aspect of data science, that includes predictive modeling. DL applications are widely used in domains such as finance, transport, healthcare, automanufacturing, and advertising. The design of the DL models based on artificial neural networks is influenced by the structure and operation of the brain. This book presents a comprehensive resource for those who seek a solid grasp of the techniques in DL.
Key features:
- Provides knowledge on theory and design of state-of-the-art deep learning models for real-world applications
- Explains the concepts and terminology in problem-solving with deep learning
- Explores the theoretical basis for major algorithms and approaches in deep learning
- Discusses the enhancement techniques of deep learning models
- Identifies the performance evaluation techniques for deep learning models
Accordingly, the book covers the entire process flow
Table of Contents
1. Introduction. 2. Concepts and Terminology. 3. State-of-the-Art Deep Learning Models: Part I. 4. State-of-the-Art Deep Learning Models: Part II. 5. Advanced Learning Techniques. 6. Enhancement of Deep Learning Architectures. 7. Performance Evaluation Techniques.
