IGI Global, 2023. — 326 p. — ISBN : 9781668469118. Social media platforms are one of the main generators of textual data where people around the world share their daily life experiences and information with online society. The social, personal, and professional lives of people on these social networking sites generate not only a huge amount of data but also open doors for...
Springer, 2022. — 271 p. — ISBN 978-3-030-75854-7. This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented...
Springer, 2018. — 512 p. — ISBN: 3319944622. This book covers both classical and modern models in deep learning. 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...
2nd Edition. — Springer, 2023. — 541 p. — ISBN 978-3-031-29642-0. Neural networks were developed to simulate the human nervous system for Machine Learning tasks by treating the computational units in a learning model in a manner similar to human neurons. The grand vision of neural networks is to create artificial intelligence by building machines whose architecture simulates...
Springer, 2021p. - 274p. - ISBN: 9783030656607 This book contributes to the progress towards intelligent transportation. It emphasizes new data management and machine learning approaches such as big data, deep learning and reinforcement learning. Deep learning and big data are very energetic and vital research topics of today’s technology. Road sensors, UAVs, GPS, CCTV and...
AI Publishing, 2020. — 293 p. — ISBN13: 978-1-7347901-2-2. Artificial intelligence is the rage today! While you may find it difficult to understand the most recent advancements in AI, it simply boils down to two most celebrated developments: Machine Learning and Deep Learning. In 2020, Deep Learning is leagues ahead because of its supremacy when it comes to accuracy, especially...
Apress, 2019. — 416 p. — ISBN: 978-1-4842-5176-8. Utilize this easy-to-follow beginner's guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks. This book begins with an explanation of...
2nd Edition. — Apress Media LLC., 2024. — 527 р. — ISBN-13: 979-8-8688-0008-5. This beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge machine learning and deep learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book,...
Springer Cham, 2023. — 94 p. — (Synthesis Lectures on Engineering, Science, and Technology) — eBook ISBN: 978-3-031-38133-1. Explores different design aspects associated with each number system and their effects on DNN performance Discusses the most efficient number systems for DNNs hardware realization Describes various number systems and their usage for Deep Neural Network...
Springer, 2025. — 128 p. — ISBN 3031860748. This book presents innovative applications of deep learning techniques in wireless ad-hoc networks, addressing critical challenges such as trust, routing, traffic management, and intrusion detection. By combining advanced AI models with real-world network scenarios, the chapters explore novel solutions for improving network...
Apress, 2020. — 356 p. — ISBN 9781484264300. Build deep learning and computer vision systems using Python, TensorFlow, Keras, OpenCV, and more, right within the familiar environment of Microsoft Windows. The book starts with an introduction to tools for deep learning and computer vision tasks followed by instructions to install, configure, and troubleshoot them. Here, you will...
Independently published, 2019. — 272 р. — ISBN: 978-1793223012. New 2019 Edition! Build Deeper is a complete and practical guide that can help you take the first few steps in deep learning. It will guide you step-by-step, from understanding the basic concepts, to building your first practical application. Deep Learning is the latest iteration of AI. Although the concept itself...
John Wiley & Sons, Inc., 2025. — 256 p. — ISBN 978-1-394-26927-3. Comprehensive, accessible introduction to deep learning for engineering tasks through Python programming, low-cost hardware, and freely available software Deep Learning on Embedded Systems is a comprehensive guide to the practical implementation of deep learning for engineering tasks through computers and...
Apress, 2018. — 343 p. Discover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples. These applications include image recognition, segmentation and annotation, video processing and annotation, voice recognition, intelligent personal assistants, automated translation, and autonomous vehicles. An...
2nd Edition. — Packt Publishing, 2020. — 503 p. — ISBN: 978-1-83882-165-4. Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised...
Packt Publishing, 2020. — 286 p. — ISBN: 978-1-78913-396-7. Work through practical recipes to learn how to automate complex machine learning and deep learning problems using Python. With artificial intelligence systems, we can develop goal-driven agents to automate problem-solving. This involves predicting and classifying the available data and training agents to execute tasks...
Mirza Rahim Baig, Thomas V. Joseph, Nipun Sadvilkar, Mohan Kumar Silaparasetty, Anthony So. — Packt Publishing Ltd., July 2020. — 474 p. — ISBN: 978-1-83921-985-6. Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret text Are you fascinated by how deep learning powers intelligent applications such as...
Packt Publishing, 2017. — 330 p. — ISBN: 178712519X. Solve different problems in modelling deep neural networks using Python, Tensorflow, and Keras with this practical guide. Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a...
Balas Valentina Emilia, Roy Sanjiban Sekhar, Sharma Dharmendra, Samui Pijush. — Springer, 2019. — 380 p. — (Smart Innovation, Systems and Technologies). — ISBN: 978-3-030-11479-4 (eBook). This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image...
John Wiley & Sons, Inc., 2023. — 256 p. — eBook ISBN: 978-1-394-16777-7. This book thoroughly explains deep learning models and how to use Python programming to implement them in applications such as NLP, face detection, face recognition, face analysis, and virtual assistance (chatbot, machine translation, etc.). It provides hands-on guidance in using Python for implementing...
IOP Publishing, 2022. — 267 p. — (IOP Series in Next Generation Computing). Artificial Intelligence (AI) is gaining traction in areas of social responsibility. From climate change to social polarization to epidemics, humankind has been seeking new solutions to these ever-present problems. Deep Learning (DL) techniques have increased in power in recent years, with algorithms...
Packt Publishing, 2018. — 272 p. — ISBN: 1788837991. Dive deeper into neural networks and get your models trained, optimized with this quick reference guide. Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It...
Apress, 2017. — 227 p. — ISBN: 978-1484227336. Understand deep learning, the nuances of its different models, and where these models can be applied. The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a...
Apress Berkeley, 2024. — 364 p. — eBook ISBN 979-8-8688-1035-0. Provides explanations on sequence models and their analysis using various datasets. Covers generative models and transformers, demonstrating their applications. Provides hands-on projects that guide you in understanding the processes of model creation, fine-tuning, and testing. This book discusses deep learning,...
Siddhartha Bhattacharyya, Vaclav Snasel, Aboul Ella Hassanien, Satadal Saha, B. K. Tripathy (Eds.). — Walter de Gruyter, 2020. — 179 p. — ISBN: 978-3110670790. This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative...
IGI Global, 2020. -351p. - 9781799827917 Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its...
Packt Publishing, 2017. — 300 p. — ISBN: 978-1-78646-582-5. Develop deep neural networks in Theano with practical code examples for image classification, machine translation, reinforcement agents, or generative models. This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models...
Manning Publications Co., 2025. — 392 p. — ISBN: 978-1617299056. A hands-on guide to powerful graph-based deep learning models. In Graph Neural Networks in Action, you will learn how to: Train and deploy a graph neural network Generate node embeddings Use GNNs at scale for very large datasets Build a graph data pipeline Create a graph data schema Understand the taxonomy of GNNs...
With contributions by Nicholas Locascio. — New York: O’Reilly Media, 2017. — 298 p. — ISBN: 978-1-491-92561-4. With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research that is paving the way for modern machine learning. This book uses exposition and examples to help you understand major concepts in this complicated...
2nd Edition. — O’Reilly Media, 2022. — 387 p. — ISBN: 978-1-492-08218-7. We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception that has powered our push toward self-driving vehicles, the ability to defeat human experts at a variety of difficult games including Go and Starcraft, and even generate essays with shockingly coherent prose....
Amazon Kindle Publishing, 2019. — 170 р. — ISBN: 1092562222. Build your Own Neural Network through easy-to-follow instruction and examples. Thanks this easy tutorial you’ll learn the fundamentals of Deep learning and build your very own Neural Network in Python using TensorFlow, Keras, PyTorch, and Theano. While you have the option of spending thousands of dollars on a big and...
IOP Publishing, 2024. — 365 p. — ISBN 978-0-7503-6242-9. This book, together with the accompanying Python codes, provides a thorough and extensive guide for mastering advanced computer vision techniques for image processing by using the open-source machine learning framework PyTorch. Known for its user-friendly interface and Python programming style, PyTorch is accessible and...
Wiley, 2021. — 435 p. — ISBN 9781119646143. Deep Learning for the Earth Sciences: Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link...
Anupam Ghosh, Jyotsna Kumar Mandal, Rajdeep Chakraborty, S. Balamurugan. — Wiley-Scrivener, 2023. — 480 p. — (Artificial Intelligence and Soft Computing for Industrial Transformation). — ISBN-13: 978-1119857211. In-depth analysis of Deep Learning-based cyber-IoT systems and security which will be the industry leader for the next ten years. Deep Learning (also known as deep...
Academic Press, 2021. — 230 p. — ISBN 978-0-323-90184-0. Deep Learning for Chest Radiographs enumerates different strategies implemented by the authors for designing an efficient convolution neural network-based computer-aided classification (CAC) system for binary classification of chest radiographs into "Normal" and "Pneumonia." Pneumonia is an infectious disease mostly...
Manning Publications, 2024. — 552 p. — ISBN 978-1617296482. Shine a spotlight into the deep learning “black box”. This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models, so you can customize, maintain, and explain them more effectively. Inside Math and Architectures of Deep Learning you will find: Math, theory, and...
Springer, 2021. — 403 p. — ISBN 978-981-16-2232-8. This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs),...
World Scientific Publishing Co. Pte. Ltd., 2021. — 327 p. — ISBN 9811218838. Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning...
Chivukula Aneesh Sreevallabh, Yang Xinghao, Liu Bo, Liu Wei, Zhou Wanlei. — Springer, 2023. — 319 p. — ISBN 978-3-030-99772-4. A critical challenge in Deep Learning is the vulnerability of Deep Learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in...
2nd Edition. — Manning Publications, 2021. — 504 p. — ISBN: 978-1617296864. Unlock the groundbreaking advances of deep learning with this extensively revised new edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world. In Deep Learning with Python, Second Edition...
Manning Publications, 2018. — 392 p. — ISBN13: 978-1617295546. Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples. Machine learning has made remarkable progress in recent years. Deep-learning systems now...
Manning, 2018. — 325 p. Artificial intelligence has made some incredible leaps. Deep learning systems now deliver near-human speech and image recognition, not to mention machines capable of beating world champion Go masters. Deep learning applies to a widening range of problems, such as question answering, machine translation, and optical character recognition. It's behind photo...
Manning Publications, 2017. — 384 p. Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran?ois Chollet, this book builds your understanding through intuitive explanations and practical examples. About the Technology Machine learning has made remarkable...
BPB Publications, 2019. — 357 p. — ISBN 978-93-88511-018. Get hands on with Kelp.Net, Microsoft’s latest Deep Learning framework Key Features Deep Learning Basics The ultimate Kelp.Net reference guide Develop state of the art deep learning applications C# deep learning code Develop advanced deep learning models with minimal code Develop your own advanced deep learning models...
LazyProgrammer, 2016. — 59 p. This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural...
Quantum Technologies, 2023. — 276 p. Dive into the world of Generative Deep Learning with Python, mastering GANs, VAEs, & autoregressive models through projects & advanced topics. Gain practical skills & theoretical knowledge to create groundbreaking AI applications. Key Features: Comprehensive coverage of deep learning and generative models. In-depth exploration of GANs, VAEs,...
The Institution of Engineering and Technology, 2020. — 329 p. — ISBN 978-1-78561-769-0. This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks. The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a renaissance in interest in neural...
Amazon Digital Services LLC, 2018. — 108 р. Deep learning is a process that widens the range of most artificial intelligence problems like speech recognition, image classification, question answering, optical character recognition, and transforming text to speech. It is true that deep learning is a complex subject to learn and understand, but it is not difficult for most...
Packt, 2020. — 364 p. — ASIN B085P1JG2W. A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key Features Understand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networks Learn the mathematical concepts needed to understand how deep learning models...
LazyProgrammer, 2016. — 71 p. Deep learning is making waves. At the time of this writing (March 2016), Google’s AlghaGo program just beat 9-dan professional Go player Lee Sedol at the game of Go, a Chinese board game. Experts in the field of Artificial Intelligence thought we were 10 years away from achieving a victory against a top professional Go player, but progress seems to...
Springer, 2018. — 338 p. — ISBN: 978-981-10-5208-8. In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as...
Packt, 2018. — 271 p. — ISBN: 1785880365. Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train,...
Springer, 2025. — 213 p. — ISBN 978-981-97-9333-4. Анализ позы человека: глубокое обучение соответствует кинематике человека в видео. With the rapid technological advancements and the proliferation of digital media in the contemporary world, the study of human behavior using computer technologies has taken on new dimensions. Among the most intriguing and consequential areas of...
PB Publications, 2023. — 237 p. — ISBN 978-93-5551-105-8. A step-by-step guide to get started with Machine Learning. Key Features - Understand different types of Machine Learning like Supervised, Unsupervised, Semi-supervised, and Reinforcement learning. - Learn how to implement Machine Learning algorithms effectively and efficiently. - Get familiar with the various libraries &...
Manning, 2020. — 297 p. — ISBN: 9781617296079. Probabilistic Deep Learning with Python shows how probabilistic deep learning models gives readers the tools to identify and account for uncertainty and potential errors in their results. Starting by applying the underlying maximum likelihood principle of curve fitting to deep learning, readers will move on to using the...
Manning Publications, 2020. — 300 p. — ISBN: 978-1617296079. About the Technology Probabilistic deep learning models are better suited to dealing with the noise and uncertainty of real world data — a crucial factor for self-driving cars, scientific results, financial industries, and other accuracy-critical applications. By utilizing probabilistic techniques, deep learning...
Addison-Wesley, 2021. — 800 p. — ISBN 9780137470198. NVIDIA's Full-Color Guide to Deep Learning with TensorFlow: All You Need to Get Started and Get Results Deep learning is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to deep learning with TensorFlow, the #1 Python library for building...
Addison-Wesley Professional, 2021. — 752 p. — ISBN 978-0137470358. NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results "To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals." -- From the foreword by Dr....
Manning, 2021. — 480 p. — ISBN: 9781617296192. The definitive M&E price book with additions to the measured works, updates to approximate estimating and new engineering features. Spon's Mechanical and Electrical Services Price Book 2021 continues to be the most comprehensive and best annual services engineering price book currently available, providing detailed pricing...
Springer Cham, 2022. — 341 p. — (Computational Intelligence Methods and Applications). — ISBN 978-3-031-04420-5. This book focuses on the applied aspects of artificial intelligence using enterprise frameworks and technologies. The book is applied in nature and will equip the reader with the necessary skills and understanding for delivering enterprise ML technologies. It will be...
Manning Publications Co, 2021. — 471 p. — ISBN 9781617298264. Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production. In Deep Learning Patterns and Practices you will learn: Internal functioning of modern convolutional neural networks Procedural reuse design pattern for CNN architectures Models...
Packt Publishing, 2019. — 126 p. — ISBN: 9781838551605. Use the serverless computing approach to save time and money Key Features Save your time by deploying deep learning models with ease using the AWS serverless infrastructure Get a solid grip on AWS services and use them with TensorFlow for efficient deep learning Includes tips, tricks and best practices on serverless deep...
USA: Applied Data Science Partners Ltd, 2019. — 330 p. — ISBN: 978-1-492-04194-8. Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most...
2nd Edition. — O’Reilly Media, Inc., 2023. — 453 p. — ISBN: 978-1-098-13418-1. Generative modeling is one of the hottest topics in artificial intelligence. Recent advances in the field have shown how it’s possible to teach a machine to excel at human endeavors–such as drawing, composing music, and completing tasks–by generating an understanding of how its actions affect its...
The MIT Press, 2023. — 440 р. — ISBN 978-0-262-54637-9. A highly accessible, step-by-step introduction to Deep Learning, written in an engaging, question-and-answer style. The Little Learner introduces Deep Learning from the bottom up, inviting students to learn by doing. With the characteristic humor and Socratic approach of classroom favorites The Little Schemer and The...
Packt Publishing, 2018. — 334 p. A hands-on guide to deep learning thats filled with intuitive explanations and engaging practical examples Key Features Designed to iteratively develop the skills of Python users who dont have a data science background Covers the key foundational concepts youll need to know when building deep learning systems Full of step-by-step exercises and...
Springer Singapore, 2024. — 201 p. — (Transactions on Computer Systems and Networks (TCSN)). — eBook ISBN 978-981-99-9672-8. Offers a special chapter devoted to performance evaluation of deep learning algorithms Demonstrates illustrative colorful block diagrams, figures, and full code examples to clearly present ideas involved Gives contents geared for both professionals and...
2nd Edition. — Amazon Digital Services LLC, 2018. — 770 р. The last decade and some, has witnessed some remarkable advancements in the area of Deep Learning. This area of Artificial intelligence (AI) has proliferated into many branches - Deep Belief Networks, Recurrent Neural Networks, Convolution Neural Networks, Adversorial Networks, Reinforcement Learning, Capsule Networks...
BPB Publications, 2023. — 477 р. — ISBN: 978-93-5551-194-2. Mathematical Codebook to Navigate Through the Fast-changing AI Landscape. Key Features - Access to industry-recognized AI methodology and deep learning mathematics with simple-to-understand examples. - Encompasses MDP Modeling, the Bellman Equation, Auto-regressive Models, BERT, and Transformers. - Detailed,...
No Starch Press, 2021. — 776 p. — ISBN 978-1718500723. A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You'll learn how to use key deep learning algorithms without the need for complex math. Ever since computers began beating us at chess, they've been getting better at a wide range of human...
MIT Press, 2016. — 802 p. — ISBN 978-0-262-33737-3. A comprehensive introduction to neural networks and deep learning by leading researchers of this field. Written for two main target audiences: university students (undergraduate or graduate) learning about machine learning, and software engineers. This is a PDF compilation of online book (www.deeplearningbook.org) Who Should...
Academic Press/Elsevier, 2023. — 270 p. — (Handbook of Statistics 48). — ISBN 9780443184314. Deep Learning, Volume 48 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Generative Adversarial Networks for Biometric Synthesis, Data Science and Pattern...
Independently published, 2020. — 159 p. — ISBN B08NCGCC5M. Are you interested in Machine Learning? Are you fascinated by how robots work? Are you ready to open up to the dynamics of technological change? Machine Learning has been approached in a definitive manner as a subset falling under a larger set of Artificial intelligence . It majorly focuses on the aspect of learning of...
Apress Media LLC, 2022. — 395 p. — ISBN-13: 978-1-4842-8148-2. Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first...
Packt, 2019. — 328 p. — ISBN: 9781789805673. Tackle the complex challenges faced while building end-to-end deep learning models using modern R libraries Learn Work with different datasets for image classification using CNNs Apply transfer learning to solve complex computer vision problems Use RNNs and their variants such as LSTMs and Gated Recurrent Units (GRUs) for sequence...
SciTech Publishing, 2021. — 897 p. — ISBN 978178561853. Novel deep learning approaches are achieving state-of-the-art accuracy in the area of radar target recognition, enabling applications beyond the scope of human-level performance. This book provides an introduction to the unique aspects of machine learning for radar signal processing that any scientist or engineer seeking...
Apress Media, LLC., 2022-12-31. — 239 p. — ISBN13: 978-1-4842-8587-9. Синтетические данные для глубокого обучения: создание синтетических данных для принятия решений и приложений с помощью Python и R Data is the indispensable fuel that drives the decision making of everything from governments, to major corporations, to sports teams. Its value is almost beyond measure. But what...
Springer, 2022. — 117 p. — (Synthesis Lectures on Computer Vision). — ISBN 978-3-031-14594-0. Методы нормализации в глубоком обучении This book presents and surveys normalization techniques with a deep analysis in training deep neural networks. In addition, the author provides technical details in designing new normalization methods and network architectures tailored to...
IGI Global, 2023. — 324. — (Advances in Finance, Accounting, and Economics (AFAE) Book Series). — ISBN 9781668444849. The advancements in Artificial Intelligence and Machine Learning have significantly affected the way financial services are offered and adopted today. Important financial decisions such as investment decision making, macroeconomic analysis, and credit evaluation...
IGI Global, 2020. — 1671 p. — ISBN: 978-1799804154. Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in...
Packt Publishing, 2020. — 263 p. — ISBN: 978-1-83882-546-1. Get to grips with building powerful deep learning models using scikit-learn and Keras One-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. As there are numerous theories about how humans perform one-shot learning, there are several...
Springer Nature Singapore Pte Ltd., 2019. — 237 p. — ISBN: 978-981-10-5152-4 (eBook). This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and...
Independently published, 2022. — 585 p. Composed for everyday programmers, there are no complicated mathematical proofs or unnecessary academic concept in Inside Deep Discovering . Trip with the theory and technique of modern deep learning, and also use ingenious methods to solve daily information issues. Inside Deep Understanding is a busy novice's guide to solving common...
Independently published, 2022. — 473 p. Computer system vision is main to several groundbreaking innovations, consisting of self-driving cars, drones, boosted truth, facial recognition, and a lot, much more . Incredible new computer system vision applications are created each day, thanks to fast developments in AI as well as deep learning (DL) . Deep Learning for Vision...
Springer, 2023. — 433 p. — e-ISBN 978-3-031-32879-4. Основы глубокого обучения This book provides a conceptual understanding of Deep Learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble...
O’Reilly Media, Inc., 2024. — 350 р. — ISBN: 978-1-098-14839-3. 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...
Packt Publishing, 2018. — 436 p. — ISBN: 178899745X. Build and deploy powerful neural network models using the latest Java deep learning libraries Key Features Understand DL with Java by implementing real-world projects Master implementations of various ANN models and build your own DL systems Develop applications using NLP, image classification, RL, and GPU processing Book...
Interviews AI. — 2020. — 411 p. — ISBN 9781916243569. Deep Learning Interviews is home to hundreds of fully-solved problems, from a wide range of key topics in AI. It is designed to both rehearse interview or exam specific topics and provide machine learning M.Sc./Ph.D. students, and those awaiting an interview a well-organized overview of the field. The problems it poses are...
BPB Publications, 2020. — 205 р. — ISBN: 978-93-89328-684. Learn modern-day technologies from modern-day technical giants DESCRIPTION The aim of this book is to help the readers understand the concept of artificial intelligence and deep learning methods and implement them into their businesses and organizations. The first two chapters describe the introduction of the artificial...
The MIT Press, 2019. — 196 p. — (MIT Press Essential Knowledge series). — ISBN: 978-0262537551. An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones,...
2nd edition. — Apress, 2021. — 316 p. — ISBN 978-1484253632. Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a...
Apress, 2017. — 169 p. — ISBN: 978-1-4842-2765-7. Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is...
Leanpub, 2023-05-31. — 163 р. (2023-05-31 Update) Zefs Guide to Deep Learning is a short guide to the most important concepts in Deep Learning, the technique at the center of the current Artificial Intelligence (AI) revolution. It will give you a strong understanding of the core ideas and most important methods and applications. All in around only 150 pages! This book presents...
BPB Publications, 2024. — 544 p. A hands-on guide to building and deploying Deep Learning models with Python. Key Features: - Acquire the skills to perform exploratory data analysis, uncover insights, and preprocess data for Deep Learning tasks. - Build and train various types of neural networks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks...
Elsevier, 2024. — 334 p. — ISBN: 978-0-443-21432-5. Applications of Deep Machine Learning in Future Energy Systems pushes the limits of current Artificial Intelligence techniques to present deep machine learning suitable for the complexity of sustainable energy systems. The first two chapters take the reader through the latest trends in power engineering and system design and...
Singapore: Springer Singapor, 2018. — 79 p. — ISBN: 978-981-13-1444-5. This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular, it discusses deep learning applications in IDSs in different...
No Starch Press, 2021. — 464 p. — ISBN 9781718500747. Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep...
No Starch Press, 2022. — 344 p. — ISBN 978-1-7185-0190-4. With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You'll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a...
O’Reilly Media, 2019. — 620 p. — ISBN: 978-1-492-03486-5. Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile,...
Addison-Wesley Professional, 2020. — 415 p. — (Addison-Wesley Data & Analytics Series). — ISBN13: 978-0-13-511669-2. Deep learning is one of today’s hottest fields. This approach to machine learning is achieving breakthrough results in some of today’s highest profile applications, in organizations ranging from Google to Tesla, Facebook to Apple. Thousands of technical...
CRC Press, 2023. — 246 р. — ISBN: 978-1-003-34868-9. Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision provides an overview of general deep learning methodology and its applications of Natural Language Processing (NLP), speech and Computer Vision tasks. It simplifies and presents the concepts of Deep Learning in a comprehensive manner, with...
Manning Publishing, 2019. — 240 p. — ISBN: 978-1617295560. GANs in Action teaches you how to build and train your own Generative Adversarial Networks, one of the most important innovations in deep learning. In this book, you'll learn how to start building your own simple adversarial system as you explore the foundation of GAN architecture: the generator and discriminator...
LazyProgrammer, 2016. — 46 p. When we talk about modern deep learning, we are often not talking about vanilla neural networks - but newer developments, like using Autoencoders and Restricted Boltzmann Machines to do unsupervised pre-training. Deep neural networks suffer from the vanishing gradient problem, and for many years researchers couldn’t get around it - that is, until new...
CRC, 2021. — 338 p. — ISBN: 9781536189896 Using the implementation of a deep learning framework as an example, C++ Template Metaprogramming in Practice: A Deep Learning Framework explains the application of metaprogramming in a relatively large project and emphasizes ways to optimize systems performance. The book is suitable for developers with a basic knowledge of C++....
Packt Publishing Ltd., 2019. — 424 p. — ISBN: 978-1-78899-808-6. Concepts, tools, and techniques to explore deep learning architectures and methodologies Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and...
Springer Nature, 2019. — 452 p. — ISBN: 978-3-030-13969-8 (eBook). This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It...
Editora Dialetica, 2022. — 121 p. Recently, Deep Learning has caused a significant impact on computer vision, speech recognition, and natural language understanding. In spite of the remarkable advances, Deep Learning (DL) recent performance gains have been modest and usually rely on increasing the depth of the models, which often requires more computational resources such as...
O’Reilly Media, Inc., 2024. — 458 p. — ISBN 978-1-098-14528-6. Bringing a deep-learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full stack deep learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. This book illustrates complex...
Apress, 2018. — 184 p. — ISBN: 148423720X. Discover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a ‘thought process’ that is capable of learning abstract concepts built from...
Apress, 2018. — 262 p. — ISBN13: (electronic): 978-1-4842-3646-8. Discover the essential building blocks of a common and powerful form of deep belief net: the autoencoder. You’ll take this topic beyond current usage by extending it to the complex domain for signal and image processing applications. Deep Belief Nets in C++ and CUDA C: Volume 2 also covers several algorithms for...
Apress, 2018. — 219 p. Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with...
CRC Press, 2024. — 199 p. — ISBN 9781032487960. Глубокое обучение: руководство для начинающих This book focuses on Deep Learning (DL), which is an important aspect of Data Science, that includes predictive modeling. DL applications are widely used in domains such as finance, transport, healthcare, automanufacturing, and advertising. The design of the DL models based on...
Anita Gehlot, Dolly Sharma, Monika Mangla, Rajesh Singh, Sergio Márquez Sánchez, Vaishali Mehta. — Bentham Science Publishers, 2022. — 228 p. — ISBN: 978-981-5036-08-4. The competence of deep learning for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement...
Packt Publishing, 2020. — 384 p. — ISBN 978-1-80056-661-3. Discover how to integrate KNIME Analytics Platform with deep learning libraries to implement artificial intelligence solutions Key Features - Become well-versed with KNIME Analytics Platform to perform codeless deep learning - Design and build deep learning workflows quickly and more easily using the KNIME GUI -...
Packt, 2018. — 442 p. — ISBN: 978-1-78839-990-6 Deep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick...
Apress, 2019. — 294 p. — ISBN: 1484249755. Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the...
Apress, 2018. — 425 p. — ISBN: 1484237897. Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish),...
Apress, 2022. - 397p. - ISBN: 1484280199 Understand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This book is designed so that you can focus on the parts you are...
Fullstack.io, 2020. — 769 p. Zero to Deep Learning is carefully designed to teach you step-by-step how to build, train, evaluate, improve and deploy deep learning models. Each chapter covers a topic and we provide full code examples as executable Jupyter notebooks. Within the first few minutes, we’ll know enough deep learning to start seeing the benefits of using it in our...
Canada: John Wiley & Sons, Inc, 2019. — 442 p. — ISBN: 978-1-119-54304-6. Take a deep dive into deep learning. Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated...
Leanpub, 2022. — 364 p. Version: 2022-10-03 Build Deep Learning applications with Keras and TensorFlow. Topics covered include Convolutional Neural networks, Recurrent Neural Networks, TensorBoard, Transfer learning, custom training loops, and Keras Functional API. Deep Learning (DL) is a branch of Machine Learning (ML) that involves building networks that try to mimic the...
Amazon Digital Services LLC, 2017. — 75 p. — ISBN: 1981614060. Neural Networks and Deep Learning: Deep Learning explained to your granny – A visual introduction for beginners who want to make their own Deep Learning Neural Network (Machine Learning) Ready to crank up a neural network to get your self-driving car pick up the kids from school? Want to add 'Deep Learning’ to your...
Loris Nanni, Sheryl Brahnam, Rick Brattin, Stefano Ghidoni, Lakhmi C. Jain. (Editors). — Springer, 2020. — 286 p. — ISBN: 978-3-030-42750-4. This book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate...
Springer, 2021. — 354 p. — ISBN 978-3-030-75177-7. This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine...
O’Reilly, 2018. — 220 p. — ISBN: 149199584X. Deep learning doesn't have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you'll learn how to solve deep-learning...
Apress, 2018. — 290 p. — ISBN: 148423684X. Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP...
Packt, 2022. — 321 p. — ISBN 180324366X. Supercharge your skills for tailoring deep-learning models and deploying them in production environments with ease and precision. Key Features Learn how to convert a deep learning model running on notebook environments into production-ready application supporting various deployment environments. Learn conversion between PyTorch and...
Pack, 2020. - 449p. - ISBN: 9781800200456 Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide Key Features Use TensorFlow to write reinforcement learning agents for performing challenging tasks Learn how to solve finite Markov decision problems...
2nd Edition. — Apress Media LLC., 2022. — 348 p. – ISBN-13: 978-1-4842-7912-0. Harness the power of MatLAB for deep-learning challenges. Practical MatLAB Deep Learning, Second Edition, remains a one-of a-kind book that provides an introduction to deep learning and using MatLAB's deep-learning toolboxes. In this book, you’ll see how these toolboxes provide the complete set of...
Apress, 2021. — 388 p. — ISBN 978-1-4842-7340-1. Use TensorFlow 2.x in the Google Colab ecosystem to create state-of-the-art deep learning models guided by hands-on examples. The Colab ecosystem provides a free cloud service with easy access to on-demand GPU (and TPU) hardware acceleration for fast execution of the models you learn to build. This book teaches you...
2nd Edition. — Apress Media, LLC., 2023. — 672 p. — ISBN13: 978-1-4842-8931-0. 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...
O’Reilly Media, 2017. — 520 p. — ISBN: 978-1-491-91425-0. Looking for one central source where you can learn key findings on machine learning? Deep Learning: A Practitioner's Approach provides developers and data scientists with the most practical information available on the subject, including deep learning theory, best practices, and use cases. Authors Adam Gibson and Josh...
BISAC: Computers / Intelligence (AI) & Semantics, 2018. — 394 p. I challenge you to find a field as interesting and exciting as Deep Learning. This book is a spin-off from my previous book "The Deep Learning AI Playbook". The Playbook was meant for a professional audience. This is targeted to a much wider audience. There are two kinds of audiences, those looking to explore and...
Pragmatic Programmers, LLC., 2020. — 342 p. — ISBN: 978-1680506600. You’ve decided to tackle machine learning – because you’re job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It’s easy to be intimidated, even as a software developer. The good news is that it doesn’t have to be that hard. Master machine learning by writing...
Springer Singapore, 2022. — 406 p. — ISBN 978-981-19-0638-1. Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously...
The MIT Press, 2023. — 541 p. — ISBN: 978-0-262-04864-4. An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Deep Learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date...
Amazon Digital Services LLC, 2019. — 89 р. — ASIN B07GTT616W. Want to learn deep learning and AI, but hate math? This book is an experiment for me. After years of teaching successful deep learning and machine learning courses online, I’ve come to notice a few patterns. One of them is that a large subset of students just RUN AWAY at the sight of math. This is somewhat problematic...
GitforGits, 2024. — 332 p. — ASIN: B0DM3K9NPC. This is the practical, solution-oriented book for every data scientists, machine learning engineers, and AI engineers to utilize the most of Google JAX for efficient and advanced machine learning. It covers essential tasks, troubleshooting scenarios, and optimization techniques to address common challenges encountered while working...
Packt Publishing, 2019. — 353 p. — ISBN: 1789538777, 978-1789538779. Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R libraries Key Features Implement deep learning algorithms to build AI models with the help of tips and tricks Understand how deep learning models operate using expert techniques Apply...
Packt Publishing, 2020. — 436 p. — ISBN: 978-1-78899-520-7. Use Java and Deeplearning4j to build robust, enterprise-grade deep learning models from scratch Java is one of the most widely used programming languages in the world. With this book, you’ll see how its popular libraries for deep learning, such as Deeplearning4j (DL4J), make deep learning easy. Starting by configuring...
BPB Publications, 2025. — 476 p. — ISBN-13: 978-93-65890-846. Description Explore the world of generative AI, a technology capable of creating new data that closely resembles reality. This book covers the fundamentals and advances through cutting-edge techniques. It also clarifies complex concepts, guiding you through the essentials of deep learning, neural networks, and the...
O’Reilly, 2019. — 253 p. — ISBN13: 978-1-492-03983-9 Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields....
Packt Publishing Ltd, 2020. — 332 p. Build autonomous vehicles using deep neural networks and behavior-cloning techniques With self-driving cars (SDCs) being an emerging subject in the field of artificial intelligence, data scientists have now focused their interest on building autonomous cars. This book is a comprehensive guide to using deep learning and computer vision...
Packt Publishing, 2017. — 288 p. — ASIN B071NDMWN2. Powerful, independent recipes to build deep learning models in different application areas using R libraries About This Book Master intricacies of R deep learning packages such as mxnet & tensorflow Learn application on deep learning in different domains using practical examples from text, image and speech Guide to set-up deep...
2nd ed. — Packt, 2017. — 501 p. — ISBN: 978-1787125933. Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book Second edition of the bestselling book on Machine Learning A practical approach to key frameworks in data science, machine learning, and deep learning Use the most powerful...
Packt Publishing, 2019. - 512p. - ISBN: 9781789344158 Understand basic to advanced deep learning algorithms, the mathematical principles behind them, and their practical applications. Key Features Get up-to-speed with building your own neural networks from scratch Gain insights into the mathematical principles behind deep learning algorithms Implement popular deep learning...
2nd edition. — Packt Publishing, 2020. — 761 p. — ISBN 9781839210686. An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithms Key Features Covers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithm Learn how to implement algorithms with code...
Independently published, 2023. — 131 p. — ASIN B0CQTJ4GNZ. About the technology: Forget predictable pixels and soulless algorithms. GANs, the renegade artists of AI, paint beyond the canvas, blurring reality with their brushstrokes of code. This book is your passport to their rebellion. Short summary: GANs & TensorFlow for Developers is your code-fueled escape hatch from the...
Packt, 2020. — 432 p. — ISBN: 9781838640859. Implementing supervised, unsupervised, and generative deep learning (DL) models using Keras, TensorFlow, and PyTorch Key Features Understand the fundamental machine learning concepts useful in deep learning Learn the underlying mathematical and statistical concepts as you implement smart deep learning models from scratch Explore...
Springer Cham, 2023. — 514 p. — (Studies in Big Data, volume 134) — eBook ISBN: 978-3-031-40688-1. Introduces machine and deep learning approaches for solving challenging problems Collects the latest technological innovations and models related to deep learning Includes representative applications and case studies using cutting-edge technologies In recent years, significant...
Morgan & Claypool, 2020. — 236 p. — (Synthesis Lectures on Computer Architecture). — ISBN 10 1681739666, 13 978-1681739663. This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The purpose of this book is to provide a solid understanding of (1) the design,...
Manning Publications Co., 2025. — 504 p. — ISBN: 978-1633438545. Business runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques. Machine Learning for Tabular Data teaches you to train insightful machine learning models on common tabular business data sources such as spreadsheets,...
Manning, 2021. - 264p. - ISBN: 9781617296727 Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques...
Manning Publications Co., 2020. — 241 p. — ISBN: 978-1617296727. Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world...
IGI Global, 2020. - 405p. - ISBN: 9781799850687 Wireless sensor networks have gained significant attention industrially and academically due to their wide range of uses in various fields. Because of their vast amount of applications, wireless sensor networks are vulnerable to a variety of security attacks. The protection of wireless sensor networks remains a challenge due to...
Packt Publishing, 2021. — 317 p. — ISBN 9781800206137. Discover ways to implement various deep learning algorithms by leveraging Python and other technologies Key Features Learn deep learning models through several activities Begin with simple machine learning problems, and finish by building a complex system of your own Teach your machines to see by mastering the technologies...
Apress 2021. — 394 p. — ISBN 978-1484268087. Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics, healthcare, finance, and many more. This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding exercise. You'll begin by reviewing the Markov decision...
Springer Cham 2021. — 104 p. — ISBN 978-3-030-94482-7. The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Differently from most of the current literature, it implements a multi-step approach, i.e., the forecast of an entire interval of future values. This is relevant for many applications, such as...
Wiley-IEEE Press, 2024. — 259 p. — ISBN: 978-1394205608. Discover how to train Deep Learning models by learning how to build real Deep Learning software libraries and verification software! The study of Deep Learning and Artificial Neural Networks (ANN) is a significant subfield of artificial intelligence (AI) that can be found within numerous fields: medicine, law, financial...
Manning Publications, 2024. — 408 p. — ISBN-13: 978-1633438880. Accelerate deep learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library. The JAX numerical computing library tackles the core performance challenges at the heart of deep learning and other scientific computing tasks. By combining Google’s Accelerated Linear...
Packt, 2019. — 242 p. — ISBN: 1789340993. Apply modern deep learning techniques to build and train deep neural networks using Gorgonia Key Features Gain a practical understanding of deep learning using Golang Build complex neural network models using Go libraries and Gorgonia Take your deep learning model from design to deployment with this handy guide Book Description Go is an...
Vivek S. Sharma, Shubham Mahajan, Anand Nayyar, Amit Kant Pandit (Editor). — CRC Press, 2025. — 390 p. — ISBN: 978-1003564874. Unlock the transformative potential of deep learning in your professional and academic endeavors with Deep Learning in Engineering, Energy and Finance: Principals and Applications. This comprehensive guide seamlessly bridges the gap between theoretical...
Springer Cham, 2023. — 161 p. — (Springer Handbooks) — eBook ISBN: 978-3-031-39244-3. Easy-to-understand description of the multiple facets of design, development and deployment of deep learning networks Practical tools that facilitate understanding of underlying technology Covers wide-ranging conceptual modeling and programming tools that animate deep learning applications...
Springer Singapore, 2025. — 610 p. — (Studies in Big Data, volume 162). — eBook ISBN 978-981-97-8019-8. Delves deeper into the mathematical intricacies of tensors in AI. Provides focused exploration of tensor calculus and its applications in deep learning. Includes summaries, illustrative examples, and exercises aimed at reinforcing the reader's understanding of the material....
Springer, 2020. — 140 p. — ISBN: 978-3-030-37591-1 (eBook). This stimulating text/reference presents a philosophical exploration of the conceptual foundations of deep learning, presenting enlightening perspectives that encompass such diverse disciplines as computer science, mathematics, logic, psychology, and cognitive science. The text also highlights select topics from the...
Springer, 2018. — 196 p. — (Undergraduate Topics in Computer Science). — ISBN: 978-3-319-73003-5. This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive...
Independently published, 2021. — 390 p. — ISBN 979-8721791499. This book is not only for programmers and IT professionals but also for businesspeople who are looking forward to boosting their average sales and customer experience. This book contains all the relevant topics that you’ll want to know about deep learning neural networks. You will learn some amazing facts about the...
Independently published, 2020. — 182 p.— ISBN B08S1C9KST. Are you interested in taking your deep learning knowledge to the next level? Then this is the book for you! Machine and deep learning are the future, and there’s no getting away from that. So learning it now, and learning how to do it the right way will put you ahead of the crowd. Deep learning is all about understanding...
Independently published, 2022. — 297 p. Learn how to fine-tune the current state-of-the-art EffecientNet V2 model to perform image classification on satellite data (EuroSAT) using TensorFlow in Python. Satellite image classification is undoubtedly crucial for many applications in agriculture, environmental monitoring, urban planning, and more. Applications such as crop...
Chapman and Hall/CRC, 2017. — 364 p. — (Machine Learning & Pattern Recognition). — ISBN: 978-1138626782. Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn’t prove theorems, or otherwise dwell...
Packt Publishing, 2017. — 744 p. — ISBN: 978-1788470315. Build and run intelligent applications by leveraging key Java machine learning libraries Develop a sound strategy to solve predictive modelling problems using the most popular machine learning Java libraries. Explore a broad variety of data processing, machine learning, and natural language processing through diagrams,...
De Gruyter, 2022. — 214 p. — ISBN 978-3-11-075061-4. Cognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing (NLP). The integration of Deep Learning (DL) improves the performance of Cognitive computing systems in many applications, helping in utilizing heterogeneous data...
IGI Global, 2023. — 400 p. — ISBN-13: 978-1668480984. Today’s business world is changing with the adoption of the internet of things (IoT). IoT is helping in prominently capturing a tremendous amount of data from multiple sources. Realizing the future and full potential of IoT devices will require an investment in new technologies. The Handbook of Research on Deep Learning...
BPB Publications, 2023. — 878 p. The book presents you with a thorough introduction to AI and Machine learning, starting from the basics and progressing to a comprehensive coverage of Deep Learning with Python. You will be introduced to the intuition of Neural Networks and how to design and train them effectively. Moving on, you will learn how to use Convolutional Neural...
Manning Publications, 2019. — 327 p. — ISBN: 1617294799. Deep Learning for Search is a practical book about how to use (deep) neural networks to help build effective search engines. This book examines several components of a search engine, providing insights on how they work and guidance on how neural networks can be used in each context. Emphasis is given to practical,...
J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant. — IGI Global, 2020. — 355 p. — ISBN: 978-1799811947 (ebook). Deep Learning Techniques and Optimization Strategies in Big Data Analytics (Advances in Systems Analysis, Software Engineering, and High Performance Computing) “This book examines the application of artificial intelligence in machine learning, data...
Manning Publications, 2023. — 623 p. — ISBN: 978-1633439023. Guide machine learning projects from design to production with the techniques in this unique project management guide. No ML skills required ! In Managing Machine Learning Projects you’ll learn essential machine learning project management techniques, including: Understanding an ML project’s requirements. Setting up...
2nd Edition: Springer, 2024. — 325 p. — ISBN 978-3-031-64086-5. This first comprehensive book on models behind Generative AI has been thoroughly revised to cover all major classes of deep generative models: mixture models, Probabilistic Circuits, Autoregressive Models, Flow-based Models, Latent Variable Models, GANs, Hybrid Models, Score-based Generative Models, Energy-based...
2018. — 204 p. Artificial Intelligence is changing our lives, and solutions based on Deep Learning are leading this transformation. Deep Learning is now of major interest to private companies, since it can be applied to many areas of activity. But getting started in this technology is not an easy task. Many enthusiastic professionals in the field of Deep Learning have difficulties...
Packt, 2020. — 442 p. — ISBN: 9781789613179. Take your quantitative strategies to the next level by exploring nine examples that make use of cutting-edge deep learning technologies, including CNNs, LSTM, GANs, reinforcement learning, and CapsNets Learn Implement quantitative financial models using the various building blocks of a deep neural network Build, train, and optimize...
John Wiley & Sons, 2023. — 544 p. — ISBN 978-1-119-84502-7. A concise and practical exploration of key topics and applications in data science In Deep Learning: From Big Data to Artificial Intelligence with R, expert researcher Dr. Stéphane Tufféry delivers an insightful discussion of the applications of deep learning and big data that focuses on practical instructions on...
CRC Press, 2022. — 140 p. — ISBN 9781003091356. Deep learning is an artificially intelligent entity that teaches itself and can be utilized to make predictions. Deep learning mimics the human brain and provides learned solutions addressing many challenging problems in the area of visual computing. From object recognition to image classification for diagnostics, deep learning...
Birmingham - Mumbai: Packt Publishing, 2019. — 386 p. — ISBN: 978-1789348460, 1789348463. 2nd Edition. Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries Key Features Build a strong foundation in neural networks and deep learning with Python libraries Explore advanced deep learning techniques and their applications...
Packt Publishing, 2020. — 702 p. — ISBN: 978-1-78995-617-7. Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this...
Bentham Books, 2023. — 270 р. — ISBN: 978-981-5079-22-7. This book is a detailed reference guide on Deep Learning and its applications. It aims to provide a basic understanding of Deep Learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters...
Manning Publications Co., 2023. — 362 p. — ISBN: 978-1633439863. A vital guide to building the platforms and systems that bring deep learning models to production. In Designing Deep Learning Systems you will learn how to: Transfer your software development skills to deep learning systems Recognize and solve common engineering challenges for deep learning systems Understand the...
Academic Press, 2019. — 283 p. — (Computer Vision and Pattern Recognition). — ISBN 978-0-12-813659-1. Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models--those that emphasize problem-specific Interpretability--with recent deep network models that have enabled a larger learning capacity and better utilization of Big...
Bentham Science Publishers, 2023. — 225 p. — eBook ISBN: 978-981-5136-98-2. Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used...
Springer, 2021. — 314 p. — ISBN: 9811567581. This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial...
Springer, 2025. — 199 p. — (Studies in Big Data 12). — ISBN 978-981-96-3497-2. This book describes novel ways of using Deep Learning to solve real-world problems. It covers advanced Deep Learning topics like neural architecture search, ensemble Deep Learning, transfer learning techniques, lightweight architectures, hybrid Deep Learning approaches, and generative adversarial...
Packt Publishing, 2018. — 284 p. Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy...
World Scientific Publishing, 2021. — 641 p. — ISBN 9811234051. This unique compendium discusses some core ideas for the development and implementation of machine learning from three different perspectives — the statistical perspective, the artificial neural network perspective and the deep learning methodology.The useful reference text represents a solid foundation in machine...
Packt Publishing, 2016. — 170 p. — ISBN: 978-1-78528-058-0. Build automatic classification and prediction models using unsupervised learning. Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures. With the superb memory management and the full integration with multi-node big...
GitforGits, 2023. — 118 p. — ISBN-13: 978-8196288358. “Google JAX Essentials” is a comprehensive guide designed for machine learning and deep learning professionals aiming to leverage the power and capabilities of Google’s JAX library in their projects. Over the course of eight chapters, this book takes the reader from understanding the challenges of deep learning and numerical...
Springer, 2024. — 194 p. — (Wireless Networks). — ISBN 978-3-031-57678-2. This book presents Deep Learning techniques for video understanding. For Deep Learning basics, the authors cover Machine Learning pipelines and notations, 2D and 3D Convolutional Neural Networks for spatial and temporal feature learning. For action recognition, the authors introduce classical frameworks...
2nd Edition. — Springer, 2023. — 222 p. — (Texts in Computer Science). — eBook ISBN: 978-981-99-4823-9. The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has...
Apress, 2021. — 463 p. — ISBN 1484274121 Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of...
2019. — 536 p. Artificial Intelligence (AI) especially Deep Learning (DL) has made tremendous progress in recent years. It start to spread in many areas, such as: image classification, voice recognition, text generation, language translation etc. As time goes by, it becomes apparent that deep learning will stay in the mainstream. As a technology people, it is time to keep...
Manning Publications, 2020. — 384 p. — ISBN: 9781617295430. Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement...
New York: Springer, 2020. — 117 p. — ISBN: 3030343758. This book provides the reader with the fundamental knowledge in the area of Deep Learning (DL) with application to visual content mining. The authors give a fresh view on Deep Learning approaches both from the point of view of image understanding and supervised Machine Learning (ML). It contains chapters which introduce...
СПб.: Питер, 2018. — 400 с.: ил. — (Библиотека программиста). — ISBN: 978-5-4461-0770-4. Глубокое обучение — Deep learning — это набор алгоритмов машинного обучения, которые моделируют высокоуровневые абстракции в данных, используя архитектуры, состоящие из множества нелинейных преобразований. Согласитесь, эта фраза звучит угрожающе. Но всё не так страшно, если о глубоком...
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