Ai Foundations Of Neural Networks PDF Download
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| Author | : Jon Adams |
| Publisher | : Green Mountain Computing |
| Total Pages | : 83 |
| Release | : |
| Genre | : Computers |
| ISBN | : |
Download AI Foundations of Neural Networks Book in PDF, ePub and Kindle
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.
| Author | : Doug Rose |
| Publisher | : Independently Published |
| Total Pages | : 70 |
| Release | : 2019-03-31 |
| Genre | : Computers |
| ISBN | : 9781092242158 |
Download Artificial Intelligence Foundations Book in PDF, ePub and Kindle
This tutorial provides introductory knowledge on Artificial Intelligence. It would come to a great help if you are about to select Artificial Intelligence as a course subject. You can briefly know about the areas of AI in which research is prospering.This tutorial is prepared for the students at beginner level who aspire to learn Artificial Intelligence.
| Author | : Nikola K. Kasabov |
| Publisher | : Marcel Alencar |
| Total Pages | : 581 |
| Release | : 1996 |
| Genre | : Artificial intelligence |
| ISBN | : 0262112124 |
Download Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering Book in PDF, ePub and Kindle
Combines the study of neural networks and fuzzy systems with symbolic artificial intelligence (AI) methods to build comprehensive AI systems. Describes major AI problems (pattern recognition, speech recognition, prediction, decision-making, game-playing) and provides illustrative examples. Includes applications in engineering, business and finance.
| Author | : Mayuri Mehta |
| Publisher | : Springer Nature |
| Total Pages | : 273 |
| Release | : 2022-10-19 |
| Genre | : Technology & Engineering |
| ISBN | : 3031128079 |
Download Explainable AI: Foundations, Methodologies and Applications Book in PDF, ePub and Kindle
This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas. The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.
| Author | : K.R. Chowdhary |
| Publisher | : Springer Nature |
| Total Pages | : 730 |
| Release | : 2020-04-04 |
| Genre | : Computers |
| ISBN | : 8132239725 |
Download Fundamentals of Artificial Intelligence Book in PDF, ePub and Kindle
Fundamentals of Artificial Intelligence introduces the foundations of present day AI and provides coverage to recent developments in AI such as Constraint Satisfaction Problems, Adversarial Search and Game Theory, Statistical Learning Theory, Automated Planning, Intelligent Agents, Information Retrieval, Natural Language & Speech Processing, and Machine Vision. The book features a wealth of examples and illustrations, and practical approaches along with the theoretical concepts. It covers all major areas of AI in the domain of recent developments. The book is intended primarily for students who major in computer science at undergraduate and graduate level but will also be of interest as a foundation to researchers in the area of AI.
| Author | : Russel R Russo |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2021-02-04 |
| Genre | : |
| ISBN | : 9781801693127 |
Download Neural Networks for Beginners Book in PDF, ePub and Kindle
Do you want to understand Neural Networks and learn everything about them but it looks like it is an exclusive club? Are you fascinated by Artificial Intelligence but you think that it would be too difficult for you to learn? If you think that Neural Networks and Artificial Intelligence are the present and, even more, the future of technology, and you want to be part of it... well you are in the right place, and you are looking at the right book. If you are reading these lines you have probably already noticed this: Artificial Intelligence is all around you. Your smartphone that suggests you the next word you want to type, your Netflix account that recommends you the series you may like or Spotify's personalised playlists. This is how machines are learning from you in everyday life. And these examples are only the surface of this technological revolution. Either if you want to start your own AI entreprise, to empower your business or to work in the greatest and most innovative companies, Artificial Intelligence is the future, and Neural Networks programming is the skill you want to have. The good news is that there is no exclusive club, you can easily (if you commit, of course) learn how to program and use neural networks, and to do that Neural Networks for Beginners is the perfect way. In this book you will learn: The types and components of neural networks The smartest way to approach neural network programming Why Algorithms are your friends The "three Vs" of Big Data (plus two new Vs) How machine learning will help you making predictions The three most common problems with Neural Networks and how to overcome them Even if you don't know anything about programming, Neural Networks is the perfect place to start now. Still, if you already know about programming but not about how to do it in Artificial Intelligence, neural networks are the next thing you want to learn. And Neural Networks for Beginners is the best way to do it. Buy Neural Network for Beginners now to get the best start for your journey to Artificial Intelligence.
| Author | : Jon Adams |
| Publisher | : Green Mountain Computing |
| Total Pages | : 90 |
| Release | : |
| Genre | : Computers |
| ISBN | : |
Download AI Foundations of Deep Learning Book in PDF, ePub and Kindle
Discover the Future with "AI Foundations Of Deep Learning" Embark on a fascinating journey into the heart of Artificial Intelligence with this captivating book. "Artificial Intelligence: Deep Learning Made Easy" is more than just a guide; it's your window into the complex yet thrilling world of AI and deep learning. Key Features: Deep Learning Demystified: Unravel the mysteries of neural networks and their striking resemblance to human brain neurons. Real-World Applications: Explore how deep learning is revolutionizing fields like healthcare, autonomous vehicles, and natural language processing through engaging case studies. Insightful Narratives: Meet the thought leaders and pioneers whose contributions have shaped the landscape of AI technology. Ethical and Societal Impacts: Delve into the ethical considerations and societal impacts of deep learning, fostering a comprehensive understanding of AI's role in our world. Accessible to All: Whether you're a student, professional, or simply an AI enthusiast, this book breaks down complex concepts into an easy-to-understand format. Inspiring and Thought-Provoking: Concludes with a reflection on deep learning's key aspects, stirring your imagination and inviting you to join the ongoing AI evolution. Product Description: "Artificial Intelligence: Deep Learning Made Easy" takes you on an enlightening exploration of the silent revolution reshaping our existence. Each chapter peels back a layer of AI's most enigmatic tool, revealing how deep learning transforms data into sophisticated learning machines. Witness firsthand the transformative power of AI in various industries. Understand how it aids in medical diagnoses, powers self-driving cars, and enables computers to communicate fluently. This book not only informs but also inspires, showcasing the collaborative spirit at the intersection of technology and human ingenuity. As a tribute to the relentless curiosity driving AI from theory to reality, this book is an invitation to participate in the dialogue shaping our future's limitless possibilities. It's an essential read for anyone interested in the impact and future of AI and deep learning. Add this book to your collection and step into the world where technology meets human ingenuity!
| Author | : Jeremy Howard |
| Publisher | : O'Reilly Media |
| Total Pages | : 624 |
| Release | : 2020-06-29 |
| Genre | : Computers |
| ISBN | : 1492045497 |
Download Deep Learning for Coders with fastai and PyTorch Book in PDF, ePub and Kindle
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
| Author | : Lingfei Wu |
| Publisher | : Springer Nature |
| Total Pages | : 701 |
| Release | : 2022-01-03 |
| Genre | : Computers |
| ISBN | : 9811660549 |
Download Graph Neural Networks: Foundations, Frontiers, and Applications Book in PDF, ePub and Kindle
Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.
| Author | : Katy Warr |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 246 |
| Release | : 2019-07-03 |
| Genre | : Computers |
| ISBN | : 1492044903 |
Download Strengthening Deep Neural Networks Book in PDF, ePub and Kindle
As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come
