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A Comprehensive Guide To Neural Network Modeling
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Book Synopsis A Comprehensive Guide to Neural Network Modeling by : Steffen Skaar
Download or read book A Comprehensive Guide to Neural Network Modeling written by Steffen Skaar and published by Nova Science Publishers. This book was released on 2020-10-26 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: As artificial neural networks have been gaining importance in the field of engineering, this compilation aims to review the scientific literature regarding the use of artificial neural networks for the modelling and optimization of food drying processes. The applications of artificial neural networks in food engineering are presented, particularly focusing on control, monitoring and modeling of industrial food processes.The authors emphasize the main achievements of artificial neural network modeling in recent years in the field of quantitative structure-activity relationships and quantitative structure-retention relationships.In the closing study, artificial intelligence techniques are applied to river water quality data and artificial intelligence models are developed in an effort to contribute to the reduction of the cost of future on-line measurement stations.
Book Synopsis Neural Networks for Statistical Modeling by : Murray Smith
Download or read book Neural Networks for Statistical Modeling written by Murray Smith and published by Van Nostrand Reinhold Company. This book was released on 1993 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Applying Neural Networks by : Kevin Swingler
Download or read book Applying Neural Networks written by Kevin Swingler and published by Morgan Kaufmann. This book was released on 1996 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed to enable the reader to design and run a neural network-based project. It presents everything the reader will need to know to ensure the success of such a project. The book contains a free disk with C and C++ programs, which implement many of the techniques discussed in the book.
Book Synopsis Neural Networks and Deep Learning by : Charu C. Aggarwal
Download or read book Neural Networks and Deep Learning written by Charu C. Aggarwal and published by Springer. This book was released on 2018-08-25 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
Book Synopsis Deep Learning by : Shriram K Vasudevan
Download or read book Deep Learning written by Shriram K Vasudevan and published by CRC Press. This book was released on 2021-12-24 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning (DL) and Machine Learning (ML) concepts. DL and ML are the most sought-after domains, requiring a deep understanding – and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and DL. Starting with the basics of neural networks, and continuing through the architecture of various types of CNNs, RNNs, LSTM, and more till the end of the book, each and every topic is given the utmost care and shaped professionally and comprehensively. Key Features Includes the smooth transition from ML concepts to DL concepts Line-by-line explanations have been provided for all the coding-based examples Includes a lot of real-time examples and interview questions that will prepare the reader to take up a job in ML/DL right away Even a person with a non-computer-science background can benefit from this book by following the theory, examples, case studies, and code snippets Every chapter starts with the objective and ends with a set of quiz questions to test the reader’s understanding Includes references to the related YouTube videos that provide additional guidance AI is a domain for everyone. This book is targeted toward everyone irrespective of their field of specialization. Graduates and researchers in deep learning will find this book useful.
Book Synopsis Modeling Brain Function by : D. J. Amit
Download or read book Modeling Brain Function written by D. J. Amit and published by Cambridge University Press. This book was released on 1989 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the most exciting and potentially rewarding areas of scientific research is the study of the principles and mechanisms underlying brain function. It is also of great promise to future generations of computers. A growing group of researchers, adapting knowledge and techniques from a wide range of scientific disciplines, have made substantial progress understanding memory, the learning process, and self organization by studying the properties of models of neural networks - idealized systems containing very large numbers of connected neurons, whose interactions give rise to the special qualities of the brain. This book introduces and explains the techniques brought from physics to the study of neural networks and the insights they have stimulated. It is written at a level accessible to the wide range of researchers working on these problems - statistical physicists, biologists, computer scientists, computer technologists and cognitive psychologists. The author presents a coherent and clear nonmechanical presentation of all the basic ideas and results. More technical aspects are restricted, wherever possible, to special sections and appendices in each chapter. The book is suitable as a text for graduate courses in physics, electrical engineering, computer science and biology.
Book Synopsis Neural Network Methods for Natural Language Processing by : Yoav Goldberg
Download or read book Neural Network Methods for Natural Language Processing written by Yoav Goldberg and published by Springer Nature. This book was released on 2022-06-01 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.
Book Synopsis Neural Networks for Applied Sciences and Engineering by : Sandhya Samarasinghe
Download or read book Neural Networks for Applied Sciences and Engineering written by Sandhya Samarasinghe and published by CRC Press. This book was released on 2016-04-19 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in
Book Synopsis An Introduction to Neural Networks by : Kevin Gurney
Download or read book An Introduction to Neural Networks written by Kevin Gurney and published by CRC Press. This book was released on 2018-10-08 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.
Book Synopsis AI Foundations of Neural Networks by : Jon Adams
Download or read book AI Foundations of Neural Networks written by Jon Adams and published by Green Mountain Computing. This book was released on with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the fascinating world of artificial intelligence with "AI Foundations of Neural Networks." This comprehensive guide demystifies the complex concepts of neural networks, offering a clear and accessible path to understanding the core principles that fuel modern AI systems. From the basic building blocks of neural networks to advanced architectures, this book is designed to provide a thorough grounding in deep learning for readers at all levels of expertise. Chapters Overview: The Neuron - The Fundamental Unit: Explore the basic structure that mimics the human brain's neurons, setting the stage for understanding how neural networks operate. Activation Functions - Bringing Neurons to Life: Learn about the functions that help neural networks make decisions, allowing them to process information in complex ways. The Anatomy of Layers: Delve into how layers of neurons work together to process data, forming the backbone of neural network architecture. Backpropagation - Learning from Errors: Understand the mechanism by which neural networks learn from their mistakes, optimizing their performance over time. Loss Functions - Measuring Performance: Discover how neural networks evaluate their accuracy and make adjustments to improve their predictions. Optimization Algorithms - The Road to Convergence: Get to grips with the strategies that guide neural networks towards making more accurate predictions. Overfitting and Generalization: Learn about the challenges of making models that perform well not just on the data they were trained on but on new, unseen data as well. Advanced Architectures: Explore the frontier of neural network design, including the latest models that drive progress in AI research. Why This Book? "AI Foundations of Neural Networks" stands out as a beacon of knowledge, transforming what might appear as a complex field into a series of comprehensible concepts. With a focus on clarity, practical insights, and intuitive understanding, this book bridges the gap between theoretical knowledge and real-world application. Whether you're a student, professional, or enthusiast eager to navigate the realm of AI, this guide illuminates the path forward. Embark on a journey through the corridors of deep learning with "AI Foundations of Neural Networks." Unlock the secrets behind the artificial intelligence technologies that are transforming our world. Your exploration of neural networks starts here. Perfect for: Students, AI professionals, tech enthusiasts, and anyone curious about the inner workings of neural networks and deep learning. Discover the principles of AI that are shaping the future. Your journey into neural networks begins now.
Download or read book Neural Networks written by Berndt Müller and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers.
Book Synopsis The Algorithmic Odyssey - A Comprehensive Guide to AI Research by : Dr. Prakash Arumugam
Download or read book The Algorithmic Odyssey - A Comprehensive Guide to AI Research written by Dr. Prakash Arumugam and published by Inkbound Publishers. This book was released on 2021-02-10 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: Embark on an extraordinary journey through the cutting-edge world of artificial intelligence with The Algorithmic Odyssey. This comprehensive guide serves as both a map and a compass for navigating the complex and rapidly evolving landscape of AI research. From the foundational principles of machine learning to the latest advancements in neural networks, this book offers a detailed exploration of the algorithms that are reshaping our world. Whether you are a seasoned researcher, a curious student, or a tech enthusiast, The Algorithmic Odyssey provides invaluable insights into the methodologies, challenges, and breakthroughs that define contemporary AI research. Discover the intricacies of supervised and unsupervised learning, delve into the depths of deep learning, and understand the transformative impact of reinforcement learning. Each chapter is meticulously crafted to offer clear explanations, practical examples, and thought-provoking discussions, making complex concepts accessible without sacrificing depth. Beyond the technicalities, The Algorithmic Odyssey also addresses the ethical, societal, and philosophical implications of AI. What does it mean to create intelligent systems? How do we ensure that these technologies benefit humanity? These questions and more are explored with rigor and sensitivity, encouraging readers to think critically about the future of AI. With contributions from leading experts in the field and a wealth of resources for further study, The Algorithmic Odyssey is an essential addition to the library of anyone passionate about the future of technology and its impact on our world. Join us on this odyssey and unlock the mysteries of artificial intelligence.
Book Synopsis Deep Learning Illustrated by : Jon Krohn
Download or read book Deep Learning Illustrated written by Jon Krohn and published by Addison-Wesley Professional. This book was released on 2019-08-05 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Book Synopsis Deep Neural Evolution by : Hitoshi Iba
Download or read book Deep Neural Evolution written by Hitoshi Iba and published by Springer Nature. This book was released on 2020-05-20 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book delivers the state of the art in deep learning (DL) methods hybridized with evolutionary computation (EC). Over the last decade, DL has dramatically reformed many domains: computer vision, speech recognition, healthcare, and automatic game playing, to mention only a few. All DL models, using different architectures and algorithms, utilize multiple processing layers for extracting a hierarchy of abstractions of data. Their remarkable successes notwithstanding, these powerful models are facing many challenges, and this book presents the collaborative efforts by researchers in EC to solve some of the problems in DL. EC comprises optimization techniques that are useful when problems are complex or poorly understood, or insufficient information about the problem domain is available. This family of algorithms has proven effective in solving problems with challenging characteristics such as non-convexity, non-linearity, noise, and irregularity, which dampen the performance of most classic optimization schemes. Furthermore, EC has been extensively and successfully applied in artificial neural network (ANN) research —from parameter estimation to structure optimization. Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN). This book brings together the recent progress in DL research where the focus is particularly on three sub-domains that integrate EC with DL: (1) EC for hyper-parameter optimization in DNN; (2) EC for DNN architecture design; and (3) Deep neuroevolution. The book also presents interesting applications of DL with EC in real-world problems, e.g., malware classification and object detection. Additionally, it covers recent applications of EC in DL, e.g. generative adversarial networks (GAN) training and adversarial attacks. The book aims to prompt and facilitate the research in DL with EC both in theory and in practice.
Book Synopsis Mastering Machine Learning: A Comprehensive Guide to Success by : Rick Spair
Download or read book Mastering Machine Learning: A Comprehensive Guide to Success written by Rick Spair and published by Rick Spair. This book was released on with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to "Mastering Machine Learning: A Comprehensive Guide to Success." In this book, we embark on an exciting journey into the world of machine learning (ML), exploring its concepts, techniques, and practical applications. Whether you are a beginner taking your first steps into the field or an experienced practitioner seeking to deepen your knowledge, this comprehensive guide will equip you with the tools, strategies, and insights needed to succeed in the ever-evolving landscape of ML. Machine learning is a rapidly advancing field that has revolutionized industries and transformed the way we tackle complex problems. From personalized recommendations and speech recognition systems to autonomous vehicles and medical diagnostics, machine learning has become an integral part of our daily lives. Its ability to analyze vast amounts of data, identify patterns, and make predictions has paved the way for groundbreaking advancements across various domains. However, mastering machine learning requires more than just understanding the algorithms and techniques. It requires a holistic approach that encompasses data collection and preparation, exploratory data analysis, model building, evaluation, deployment, and continuous learning. It also demands a deep understanding of the ethical and social implications of machine learning, ensuring responsible and fair use of this powerful technology. In this book, we have carefully crafted 20 comprehensive chapters that cover a wide range of topics, from the fundamentals of machine learning to advanced techniques and future trends. Each chapter provides a deep dive into a specific aspect of machine learning, offering tips, recommendations, and strategies for success. You will learn about various algorithms, data preprocessing techniques, model evaluation methods, interpretability approaches, and much more. Throughout the book, we emphasize a practical approach to machine learning. Real-world examples, case studies, and hands-on exercises are incorporated to help you gain a deeper understanding of the concepts and apply them to your own projects. We believe that active learning and practical experience are crucial for mastering machine learning, and we encourage you to explore, experiment, and build your own models. While this book serves as a comprehensive guide, it is important to note that machine learning is a rapidly evolving field. New algorithms, techniques, and technologies are constantly emerging, and staying up-to-date with the latest advancements is essential. However, the principles and foundations discussed in this book will provide you with a solid framework to adapt and navigate the ever-changing landscape of machine learning. Whether you are an aspiring data scientist, a software engineer, a researcher, or a business professional, this book is designed to be your trusted companion in your journey to mastering machine learning. By the time you reach the end, you will have gained a deep understanding of the fundamental concepts, acquired practical skills for applying machine learning in real-world scenarios, and developed the mindset needed to tackle complex challenges and drive innovation. Get ready to embark on an exciting adventure into the world of machine learning. Let's begin our journey towards mastering machine learning and unlocking its full potential. Happy learning!
Book Synopsis Artificial Neural Network Modelling by : Subana Shanmuganathan
Download or read book Artificial Neural Network Modelling written by Subana Shanmuganathan and published by Springer. This book was released on 2016-02-03 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.
Book Synopsis Mathematical Methods for Neural Network Analysis and Design by : Richard M. Golden
Download or read book Mathematical Methods for Neural Network Analysis and Design written by Richard M. Golden and published by MIT Press. This book was released on 1996 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: For convenience, many of the proofs of the key theorems have been rewritten so that the entire book uses a relatively uniform notion.
