Название: Many-Sorted Algebras for Deep Learning & Quantum Technology Автор: Сhаrlеs R. Giаrdinа Издательство: Morgan Kaufmann/Elsevier Год: 2024 Страниц: 423 Язык: английский Формат: pdf (true), epub (true) Размер: 14.2 MB Many-Sorted Algebras for Deep Learning and Quantum Technology presents a precise and rigorous description of basic concepts in Quantum technologies and how they relate to Deep Learning and Quantum Theory. Current merging of Quantum Theory and Deep Learning techniques provides a need for a text that can give readers insight into the algebraic underpinnings of these disciplines. Although analytical, topological, probabilistic, as well as geometrical concepts are employed in many of these areas, algebra exhibits the principal thread. This thread is exposed using Many-Sorted Algebras (MSA). In almost every aspect of Quantum Theory as well as Deep Learning more than one sort or type of object is involved. For instance, in Quantum areas Hilbert spaces require two sorts, while in affine spaces, three sorts are needed. Both a global level and a local level of precise specification is described using MSA.
Название: From Deep Learning to Rational Machines: What the History of Philosophy Can Teach Us about the Future of Artificial Intelligence Автор: Саmеrоn J. Вuсknеr Издательство: Oxford University Press Год: 2024 Страниц: 301 Язык: английский Формат: epub (true) Размер: 14.1 MB This book provides a framework for thinking about foundational philosophical questions surrounding the use of deep artificial neural networks ("Deep Learning") to achieve Artificial Intelligence. Specifically, it links recent breakthroughs to classic works in empiricist philosophy of mind. In recent assessments of Deep Learning's potential, scientists have cited historical figures from the philosophical debate between nativism and empiricism, which concerns the origins of abstract knowledge. These empiricists were faculty psychologists; that is, they argued that the extraction of abstract knowledge from experience involves the active engagement of psychological faculties such as perception, memory, imagination, attention, and empathy. This book explains how recent Deep Learning breakthroughs realized some of the most ambitious ideas about these faculties from philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke, David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and Computer Science: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to hit, and philosophers can see how some of the historical empiricists' most ambitious speculations can now be realized in specific computational systems. The Chapter 1 provides an overview of Deep Learning and its characteristic strengths and weaknesses. The remaining chapters of the book offer an empiricist account of a mental faculty and explore attempts to integrate aspects of the faculty into a Deep Learning architecture.
Название: Building Intelligent Systems Using Machine Learning and Deep Learning: Security, Applications and Its Challenges Автор: Аbhауа Кumаr Sаhоо, Сhittаrаnjаn Рrаdhаn, Вhаbаni Shаnkаr Рrаsаd Мishrа Издательство: Nova Science Publishers Год: 2024 Страниц: 238 Язык: английский Формат: pdf (true) Размер: 10.8 MB The primary objective of this book is to provide insight into the design and development of the intelligent system. The proposed book volume mainly focuses on a Machine Learning and Deep Learning-based intelligent system that would bring out the latest trends in the field of tourism, healthcare, agriculture, etc. This book provides security solutions for the intelligent system in different applications. The technological gaps between the traditional system and intelligent system are mentioned in the book, which will help in better understanding for the implementation of the intelligent system using Machine Learning (ML) and Deep Learning (DL) approaches. Although ML and DL have made great achievements in intelligent systems, there are still substantial open challenges that have not been fully studied. The main open challenges of using ML and DL in intelligent systems are: (i) Better performance of the system (ii) Time complexity of the jobs running inside an intelligent system (iii) Managing overload tasks (iv) Providing security towards the system. This book will definitely help academicians, researchers and industry people towards the security, design and development of the intelligent system.
Название: Machine Learning and Deep Learning in Neuroimaging Data Analysis Автор: Аnithа S. Рillаi, Вindu Меnоn Издательство: CRC Press Год: 2024 Страниц: 133 Язык: английский Формат: pdf (true) Размер: 10.1 MB Machine Learning (ML) and Deep Learning (DL) have become essential tools in healthcare. They are capable of processing enormous amounts of data to find patterns and are also adopted into methods that manage and make sense of healthcare data, either electronic healthcare records or medical imagery. This book explores how ML/DL can assist neurologists in identifying, classifying or predicting neurological problems that require neuroimaging. With the ability to model high-dimensional datasets, supervised learning algorithms can help in relating brain images to behavioral or clinical observations and unsupervised learning can uncover hidden structures/patterns in images. Bringing together Artificial Intelligence (AI) experts as well as medical practitioners, these chapters cover the majority of neuro problems that use neuroimaging for diagnosis, along with case studies and directions for future research.
Название: Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python Автор: Sоfiеn Кааbаr Издательство: O’Reilly Media, Inc. Год: 2024 Страниц: 350 Язык: английский Формат: epub (true) Размер: 10.1 MB Deep Learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on Machine Learning and reinforcement learning. Sofien Kaabar—financial author, trading consultant, and institutional market strategist—introduces Deep Learning strategies that combine technical and quantitative analyses. By fusing Deep Learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization. Deep Learning is a slightly more complex and more detailed field than Machine Learning. Machine Learning and Deep Learning both fall under the umbrella of Data Science. As you will see, Deep Learning is mostly about neural networks, a highly sophisticated and powerful algorithm that has enjoyed a lot of coverage and hype, and for good reason: it is very powerful and able to catch highly complex nonlinear relationships between different variables. The book assumes you have basic background knowledge in both Python programming (professional Python users will find the code very straightforward) and financial trading. I take a clear and simple approach that focuses on the key concepts so that you understand the purpose of every idea.
Название: Python AI Programming: Navigating fundamentals of ML, Deep Learning, NLP, and reinforcement learning in practice Автор: Раtriсk J Издательство: GitforGits Год: 2024 Страниц: 295 Язык: английский Формат: pdf, epub (true) Размер: 10.1 MB This book aspires young graduates and programmers to become AI engineers and enter the world of Artificial Intelligence (AI) by combining powerful Python programming with Artificial Intelligence. Beginning with the fundamentals of Python programming, the book gradually progresses to Machine Learning, where readers learn to implement Python in developing predictive models. The book provides a clear and accessible explanation of Machine Learning, incorporating practical examples and exercises that strengthen understanding. We go deep into Deep Learning, another vital component of AI. Readers gain a thorough understanding of how Python's frameworks and libraries can be used to create sophisticated neural networks and algorithms, which are required for tasks such as image and speech recognition. Natural Language Processing (NLP) is also covered in the book, with fundamental concepts and techniques for interpreting and generating human-like language covered. Our adventure starts with a detailed overview of Python's principles, revealing how this language is the ideal toolkit for aspiring AI practitioners. As we progress, the domains of Machine Learning and Deep Learning unveil themselves, illustrating how Python's libraries and frameworks are crucial in pioneering advances in these fields. Each chapter advances your AI learning curve, from the fundamentals of data management to the complexity of neural networks.
Название: Pro Deep Learning with TensorFlow 2.0: A Mathematical Approach to Advanced Artificial Intelligence in Python, Second Edition Автор: Sаntаnu Раttаnауаk Издательство: Apress Год: 2023 Страниц: 667 Язык: английский Формат: pdf (true) Размер: 15.9 MB This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0. Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of Deep Learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. You’ll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, you’ll explore unsupervised learning frameworks that reflect the current state of Deep Learning methods, such as autoencoders and variational autoencoders.
Название: Deep Learning for Engineers Автор: Таriq М. Аrif, Мd Аdilur Rаhim Издательство: CRC Press Год: 2024 Страниц: 170 Язык: английский Формат: pdf (true) Размер: 18.9 MB Deep Learning for Engineers introduces the fundamental principles of Deep Learning along with an explanation of the basic elements required for understanding and applying Deep Learning models. As a comprehensive guideline for applying Deep Learning models in practical settings, this book features an easy-to-understand coding structure using Python and PyTorch with an in-depth explanation of four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning. The fundamentals of convolutional neural network (CNN) and recurrent neural network (RNN) architectures and their practical implementations in science and engineering are also discussed. Some basic knowledge of Python programming is required to follow this book. However, no chapter is devoted to teaching Python programming. Instead, we demonstrated relevant Python commands followed by brief descriptions throughout this book. A common roadblock to exploring the deep learning field by engineering students, researchers, or non-data science professionals is the variation of probabilistic theories and the notations used in Data Science or Computer Science books. In order to avoid this complexity, in this book, we mainly focus on the practical implementation part of deep learning theory using Python programming. This book includes exercise problems for all case studies focusing on various fine-tuning approaches in Deep Learning. Science and engineering students at both undergraduate and graduate levels, academic researchers, and industry professionals will find the contents useful.
Название: Session-Based Recommender Systems Using Deep Learning Автор: Rеzа Rаvаnmеhr, Rеzvаn Моhаmаdrеzаеi Издательство: Springer Год: 2024 Страниц: 314 Язык: английский Формат: pdf (true) Размер: 28.9 MB This book focuses on the widespread use of deep neural networks and their various techniques in session-based recommender systems (SBRS). It presents the success of using Deep Learning techniques in many SBRS applications from different perspectives. For this purpose, the concepts and fundamentals of SBRS are fully elaborated, and different Deep Learning techniques focusing on the development of SBRS are studied. Among the various Machine Learning algorithms, Deep Learning has recently been dramatically used in different scopes. Deep Learning models have been significantly employed in effectively extracting hidden patterns from vast amounts of data and modeling interdependent variables to solve complex problems. Since this book aims to discuss the session-based recommender system approaches using Deep Learning models, brief explanations of various deep neural networks are provided in the Chapter 1. For this purpose, the history, basic concepts, advantages/applications, and fundamental models of Deep Learning are discussed. This book aims at researchers who intend to use Deep Learning models to solve the challenges related to SBRS. The target audience includes researchers entering the field, graduate students specializing in recommender systems, web data mining, information retrieval, or machine/deep learning, and advanced industry developers working on recommender systems.
Название: Deep Learning for Multimedia Processing Applications: Volume 1: Image Security and Intelligent Systems for Multimedia Processing Автор: Uzаir Аslаm Вhаtti, Меngхing Нuаng Издательство: CRC Press Год: 2024 Страниц: 313 Язык: английский Формат: pdf (true) Размер: 31.8 MB Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of Deep Learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of Deep Learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing (NLP). Divided into two volumes, Volume One begins by introducing the fundamental concepts of Deep Learning, providing readers with a solid foundation to understand its relevance in multimedia processing. Readers will discover how Deep Learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of Deep Learning in extracting meaningful information from videos. Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using Deep Learning models. It demonstrates how Deep Learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of Deep Learning with natural language processing (NLP) techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts.
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