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...
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...
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,...
Apress, 2019. — 390 p. — ISBN13: (electronic): 978-1-4842-5177-5. 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...
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...
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...
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...
Apress Media LLC, 2024. — 384 p. — ISBN-13: 979-8-8688-1034-3. This book discusses deep learning, from its fundamental principles to its practical applications, with hands-on exercises and coding. It focuses on deep learning techniques and shows how to apply them across a wide range of practical scenarios. The book begins with an introduction to the core concepts of deep...
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...
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...
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....
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...
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...
World Scientific Publishing Co. Pte. Ltd., 2021. — 324 p. — ISBN-13: 9789811218835. 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...
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, 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, 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 Publications, 2017. — 384 p. — ISBN13: 9781617294433. Целевая аудитория: опытные разработчики. В данной книге изучаются методы глубокого обучения с использованием популярной в настоящее время библиотеки Keras. Книга написана создателем этой библиотеки и содержит многочисленные практические примеры по её применению. Также вместе с автором вы изучите концепции создания...
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...
Amazon Digital Services LLC, 2018. — 98 p. — ASIN: B07K2Q6DXH. 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...
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...
Packt Publishing, 2018. — 420 p. — ISBN: 978-1-78588-036-0. Get to grips with the essentials of deep learning by leveraging the power of Python 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...
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 Publications Co., 2020. — 297 p. — ISBN: 9781617296079. Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability teaches the increasingly popular probabilistic approach to deep learning that allows you to refine your results more quickly and accurately without much trial-and-error testing. Emphasizing practical techniques that use the Python-based...
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...
Manning Publications Co., 2021. — 475 p. — ISBN: 978-1617296192. Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems...
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...
Applied Data Science Partners Ltd, 2019. — 308 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 impressive...
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...
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...
BPB Publications, 2023. — 301 p. — ISBN: 978-93-5551-800-2. A step-by-step guide that will teach you how to deploy TinyML on microcontrollers. Key Features: - Deploy machine learning models on edge devices with ease. - Leverage pre-built AI models and deploy them without writing any code. - Create smart and efficient IoT solutions with TinyML. Description: TinyML, or Tiny...
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...
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...
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...
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. — 496 p. — ISBN: 978-1-78899-745-4. Java Deep Learning Projects: Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs Build and deploy powerful neural network models using the latest Java deep learning libraries Java is one of the most widely used programming languages. With the rise of deep learning, it has become...
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,...
Apress, 2017. — 169 p. — ISBN13: (electronic): 978-1-4842-2766-4. 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...
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 р. — ISBN 978-93-55515-391. 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...
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...
No Starch Press, 2021. — 464 p. — ISBN-13: 978-1-7185-0075-4 (ebook). 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...
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...
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...
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. — 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. — 165 p. — ISBN13: (electronic): 978-1-4842-3721-2. 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...
Apress, 2018. — 200 p. — ISBN13: (electronic): 978-1-4842-3591-1. 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...
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 -...
Apress, 2018. — 425 p. — ISBN13: 978-1-4842-3790-8. 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...
3rd ed. — Packt, 2020. — 495 p. — ISBN: 978-1800562967. Discover how to leverage Keras, the powerful and easy-to-use open-source Python library for developing and evaluating deep learning models Key Features Get to grips with various model evaluation metrics, including sensitivity, specificity, and AUC scores Explore advanced concepts such as sequential memory and sequential...
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...
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 with...
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 (eBook). 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...
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...
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...
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...
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...
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....
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...
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...
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 Publications, 2020. — 266 p. — ISBN 1617296724, 9781617296727. 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...
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...
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...
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 Publishing, 2019. — 314 p. — ISBN: 978-1-78934-099-0. Apply modern deep learning techniques to build and train deep neural networks using Gorgonia Go is an open source programming language designed by Google for handling large-scale projects efficiently. The Go ecosystem comprises some really powerful deep learning tools such as DQN and CUDA. With this book, you’ll be...
Packt Publishing, 2019. — 303 p. — ISBN: 978-1-78934-099-0. 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 open source programming language designed by Google for handling...
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, 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. — 119 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...
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...
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...
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...
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...
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...
Apress Media LLC., 2021. — 630 p. — e-ISBN: 978-1-4842-6513-0. Implement deep learning applications using TensorFlow while learning the “why” through in-depth conceptual explanations. You’ll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these...
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...
Springer, 2020. — 169 p. — ISBN: 978-3-030-34376-7 (eBook). This book provides the reader with the fundamental knowledge in the area of deep learning 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. It contains chapters which introduce...
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