This book has a wider scope as it not only covers deep learning problems in Engineering, Energy and Finance sectors but also it can be used as learning or teaching material for beginners in deep learning. It will contain ample illustrations to cater readers from interest domains ranging from pure deep learning to solar energy, different engineering branches to financial domain. The aim of the book is to provide a ready resource of reference to students, faculties and researchers working in the domain of deep learning. Deep learning and Computer Vision based research; Rapid prototyping and product development is extensively done in various automation industries with applications ranging from smart and intelligent manufacturing, human decision boosting and process innovation. This book is helpful for academicians and researchers alike. This also aims to providing a resource for engineering professionals, early career researchers a like working in the deep learning domain.

Book chapters are welcome,  not limited to the following are:

1. Fundamentals of Deep Learning: Linear Algebra and Differential Calculus

2. Model Evaluation and Hyper-parameter Tuning Techniques

3. Artificial Neural Networks: Learning Rules, Single and Multi-layer Perceptron

4. Convolution Neural Networks: concepts and applications in Solar Energy, Engineering and Finance

5. Recurrent Neural Networks in Solar Energy, Engineering and Finance

6. Long Short-Term Memory (LSTM) concepts and use cases in Solar Energy, Engineering and Finance

7.    Self-Organizing Maps: Workflow, Reading and use cases in Solar Energy, Engineering and Finance

8. Auto-Encoders and Reinforcement learning: Concepts, illustrations and applications in Solar Energy, Engineering and Finance

9. Q-learning and Representation learning: Concepts, Mathematics and use cases in Solar Energy, Engineering and Finance

10.  Deep Convolution Models: Basic definitions, types and applications in Solar Energy, Engineering and Finance

11.  Boltzmann Machine: Types, Architectures, implementations and applications in Solar Energy, Engineering and Finance

12.  Generative Adversarial Networks (GANs): Definitions, concepts and use cases in Solar Energy, Engineering and Finance

13.  Graphical Neural Networks (GNNs): Definitions, concepts and use cases in Solar Energy, Engineering and Finance

14.  Deployment of Machine Learning Models

15.  Deployment of Deep Learning Models

Important Dates and Guidelines

Abstract Submission : 10th August, 2023

Abstract Acceptance: 15th August, 2023

Full Chapter Submission: 25th September, 2023

Chapter Acceptance: 8th October, 2023

Final Chapter Submission: 31st October, 2023

Guidelines For Abstract:

All the abstracts must be submitted via email: bookchapterai@gmail.com and cc to: anand_nayyar@yahoo.co.in 

At the time of ABSTRACT SUBMISSION, submit the following information:

1. Title---Make sure that it matches, the core theme of the book

2. Author details- Name, Department Name, Institute Name and Email Address

3. Abstract --- Min 200-250 words

4. 6 Keywords

5. Table of Contents- Tentative

Guidelines For Chapter: 

All Chapters should be min 30-40 Pages, without references and Plagiarism should be less than 20%. And min 45-60 References, English Language should be good. And should comprehensively cover the content. 

 The book will be published under the CRC Press and will be submitted for possible indexing to Scopus.