The focus on the theory, algorithms, implementations and practical applications of deep learning and neural networks makes An Insight into Deep Learning and Neural Networks useful for students of Computer Science and mathematics. The book introduces neural networks starting with a quick tour of the very first ANN architectures, then covering topics such as training nets, recurrent neural networks, and reinforcement learning. Where possible, an application -centric view is highlighted to provide an understanding of the practical uses of each class of techniques.

Students of Computer Science and other related natural sciences will find it easy -to- read textbook, excellent for self -study, a high school level Knowledge of  mathematics  being the only pre-requisite to understand the material.

Salient Features of the Book:

 1. The language is simple and easily understandable.

 2. Includes hands-on approach for learning the subject.

 3. Simple and intuitive discussions of neural networks and deep learning.

 4. Provides mathematical details without losing the reader in complexity.

 5. Include exercises and examples.

 6. Discusses both traditional neural networks and recent deep learning models.

 7. Covers both classical and modern models in deep learning.

 8. An application-centric view is highlighted to provide an understanding of the practical uses of each class of techniques.

 9. Greater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pretrained language models.