Best Reference Books – Artificial Intelligence and Neural Networks

«
»
We have compiled the list of Top 10 Best Reference Books on Artificial Intelligence and Neural Networks subject. These books are used by students of top universities, institutes and colleges. Here is the full list of top 10 best books on Artificial Intelligence and Neural Networks along with reviews.

Kindly note that we have put a lot of effort into researching the best books on Artificial Intelligence and Neural Networks subject and came out with a recommended list of top 10 best books. The table below contains the Name of these best books, their authors, publishers and an unbiased review of books on "Artificial Intelligence and Neural Networks" as well as links to the Amazon website to directly purchase these books. As an Amazon Associate, we earn from qualifying purchases, but this does not impact our reviews, comparisons, and listing of these top books; the table serves as a ready reckoner list of these best books.

1. “Make Your Own Neural Network: A Gentle Journey Through the Mathematics of Neural Networks, and Making Your Own Using the Python Computer Language” by Tariq Rashid

“Make Your Own Neural Network: A Gentle Journey Through the Mathematics of Neural Networks, and Making Your Own Using the Python Computer Language” Book Review: This book provides a detailed study of the mathematics of neural networks. It also describes how to make neural networks using the Python computer language. The book starts with the basics and then goes on to describe how the neural networks work. The first part introduces the mathematical ideas underlying the neural network. In the next part, it introduces the popular and easy way to learn the Python programming language. The readers get to know how to build up a neural network which can learn to recognise human handwritten numbers. The third part extends further the practical ideas discussed in part two. The combination of diagrams and mathematics help to clear the concepts in a concise way. It is useful for beginners in neural networks.

2. “Artificial Intelligence and Neural Networks” by Dr K Uma Rao

advertisement
“Artificial Intelligence and Neural Networks” Book Review: This book provides the fundamental concepts of Artificial Intelligence and Artificial Neural Networks. The first five chapters deal with artificial intelligence and the next 6 chapters deal with neural networks. The first part of the book covers the introduction and history of AI, applications of AI, modelling of intelligent systems and their characterization using PEAS. Search algorithms, Propositional logic and First Order Predicate Logic are also discussed. The second part covers topics in neural networks such as the evolution of ANN, McCulloch Pitts Model, Learning paradigms, learning tasks, perception, etc. The diagrams given in the book provide technical understanding of the subject matter. The book is a useful learning source for students of undergraduate courses in electrical science.

3. “Artificial Intelligence and Neural Networks” by F Acar Savaci

“Artificial Intelligence and Neural Networks” Book Review: This book contains the thorough proceedings of the 14th Turkish Symposium on Artificial Intelligence and Neural Networks. It provides lecture notes in several topics of computer science. The book presents 26 revised full papers in different topics. The topics covered in the book are robotics, image processing, classification, learning theory and support vector machines. Besides, fuzzy neural networks, robotics, fuzzy logic, machine learning, engineering applications, and neural networks architecture are also included in the book. The book is useful for specialists in the area of artificial intelligence and neural networks.

4. “VLSI for Artificial Intelligence and Neural Networks” by Jose G Delgado-Frias and W R Moore

advertisement
advertisement
“VLSI for Artificial Intelligence and Neural Networks” Book Review: This book is a selection of the papers presented at the International Workshop on VLSI for Artificial Intelligence and Neural Networks. The readers get to know about the increasing complexity of artificial intelligence and neural networks algorithms. The book describes the increasing applications of these systems. The readers learn about the requirements of artificial intelligence namely- symbolic manipulation, knowledge representation, non-deterministic computations and dynamic resource allocation. The book ends with a description of constraints and demands that are imposed on the computer architectures for these applications. It provides a practical knowledge of artificial intelligence systems. The book will benefit students and practising engineers dealing with software engineering and computer science.

5. “Applied Artificial Intelligence: A compact introduction to neural networks and deep learning” by Wolfgang Beer

“Applied Artificial Intelligence: A compact introduction to neural networks and deep learning” Book Review: This book covers the basics of Artificial Neural Networks (ANN). The first part explains how to implement neural networks in Python and apply this technique to any given problem. In step-by-step examples, the reader learns how to implement neural networks in Python and to solve non-linear problems. The book explains how neural networks are built, trained with sample data sets and how these networks are capable of solving complex problems. The second part shows how to build machine learning models in Google TensorFlow and how to bring Artificial Intelligence into production. The last part deals with practical and machine learning examples. The book is useful for students and practising engineers working with software engineering, python and computer science.

6. “Neural Networks: Artificial Intelligence and Industrial Applications” by Bert Kappen and Stan Gielen

advertisement
“Neural Networks: Artificial Intelligence and Industrial Applications” Book Review: This book contains papers presented at the Third Annual SNN Symposium on Neural Networks. The papers are divided into two sections- the first gives an overview of new developments in neurobiology, the cognitive sciences, robotics, vision and data modelling. The second presents working neural network solutions to real industrial problems, including process control, finance and marketing. The book gives a comprehensive view of the state of the art in 1995. It is useful for postgraduate students and academic as well as industrial researchers. Through case studies, the book attempts to provide a practical understanding of neural networks.

7. “Neural Networks and Artificial Intelligence” by Vladimir Golovko and Akira Imada

“Neural Networks and Artificial Intelligence” Book Review: This book is a compilation of the proceedings of the 8th International Conference on Neural Networks and Artificial Intelligence. The nineteen papers provide a comprehensive overview in several topics. The papers are organized in sections covering different topics such as forest resource management, artificial intelligence by neural networks, optimization and classification. Other topics like fuzzy approach, machine intelligence, analytical approach, mobile robot and real world application are also described thoroughly. The examples and pictures provide a practice-oriented understanding of the subject matter. The book is useful for software engineers, researchers and students in the field of computer science engineering.

8. “Artificial Neural Networks: An Introduction to ANN Theory and Practice” by P J Braspenning and F Thuijsman

advertisement
“Artificial Neural Networks: An Introduction to ANN Theory and Practice” Book Review: This book presents a compilation of tutorial lectures given during a school on Artificial Neural Networks for the industrial world. The major ANN architectures are discussed to show their powerful possibilities for empirical data analysis, particularly in situations where other methods seem to fail. Theoretical insight is offered by examining the underlying mathematical principles in a detailed manner. Practical experience is provided by discussing several real-world applications such areas as control, optimization, pattern recognition, software engineering, robotics, operations research, and CAM. The book thus presents a blend of theoretical and practice-oriented learning. It will benefit researchers, graduate students and practising engineers in the field of computer science and software engineering.

9. “Artificial Neural Networks (Methods in Molecular Biology)” by Hugh Cartwright

“Artificial Neural Networks (Methods in Molecular Biology)” Book Review: This book provides a practical understanding of how ANNs are applied in biological sciences and related areas. The chapters included in the book focus on the analysis of intracellular sorting information, prediction of the behavior of bacterial communities and biometric authentication. In addition, the book discusses studies of Tuberculosis, gene signatures in breast cancer classification, use of mass spectrometry in metabolite identification, visual navigation, and computer diagnosis. For more convenience, chapters include introductions to their respective topics. The book includes application details for the readers and tips on troubleshooting as well. It will be a helpful learning source for scientists studying artificial neural networks.

10. “Fundamentals of the New Artificial Intelligence: Neural, Evolutionary, Fuzzy and More (Texts in Computer Science)” by Toshinori Munakata

advertisement
“Fundamentals of the New Artificial Intelligence: Neural, Evolutionary, Fuzzy and More (Texts in Computer Science)” Book Review: This book focuses on five important areas in computer science: neural networks, genetic algorithms, fuzzy systems, rough sets, and chaos. It provides a comprehensive view of soft computing. The readers explore the importance of these topics for real-world applications. The book describes the core principles, concepts, and technologies in the subject of new artificial intelligence. The book is useful for undergraduates and graduates studying intelligent computing, soft computing, neural networks, evolutionary computing, and fuzzy systems. In addition, it will also benefit researchers in many related areas.

People who are searching for Free downloads of books and free pdf copies of these top 10 books on Artificial Intelligence and Neural Networks – we would like to mention that we don’t have free downloadable pdf copies of these good books and one should look for free pdf copies from these Authors only if they have explicitly made it free to download and read them.

We have created a collection of best reference books on "Artificial Intelligence and Neural Networks" so that one can readily see the list of top books on "Artificial Intelligence and Neural Networks" and buy the books either online or offline.

If any more book needs to be added to the list of best books on Artificial Intelligence and Neural Networks subject, please let us know.

Sanfoundry Global Education & Learning Series – Best Reference Books!

Participate in the Sanfoundry Certification contest to get free Certificate of Merit. Join our social networks below and stay updated with latest contests, videos, internships and jobs!
advertisement
advertisement
Manish Bhojasia - Founder & CTO at Sanfoundry
Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He is Linux Kernel Developer & SAN Architect and is passionate about competency developments in these areas. He lives in Bangalore and delivers focused training sessions to IT professionals in Linux Kernel, Linux Debugging, Linux Device Drivers, Linux Networking, Linux Storage, Advanced C Programming, SAN Storage Technologies, SCSI Internals & Storage Protocols such as iSCSI & Fiber Channel. Stay connected with him @ LinkedIn | Youtube | Instagram | Facebook | Twitter