Ivan Vasilev's Books

Python Deep Learning

Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow

By: Ivan Vasilev,Daniel Slater,Gianmario Spacagna,Peter Roelants,Valentino Zocca

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Python Deep Learning

Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow

By: Ivan Vasilev,Daniel Slater,Gianmario Spacagna,Peter Roelants,Valentino Zocca

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 across computer vision and NLP Learn how a computer can navigate in complex environments with reinforcement learning Book DescriptionWith the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.What you will learn Grasp the mathematical theory behind neural networks and deep learning processes Investigate and resolve computer vision challenges using convolutional networks and capsule networks Solve generative tasks using variational autoencoders and Generative Adversarial Networks Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models Explore reinforcement learning and understand how agents behave in a complex environment Get up to date with applications of deep learning in autonomous vehicles Who this book is for This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.

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379

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Packt Publishing Ltd

Published Date

ISBN 10

1789349702

ISBN 13

9781789349702

Advanced Deep Learning with Python

Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

By: Ivan Vasilev

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Advanced Deep Learning with Python

Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

By: Ivan Vasilev

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 Key FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook Description 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 book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.

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456

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Packt Publishing Ltd

Published Date

ISBN 10

1789952719

ISBN 13

9781789952711

Explosion Systems with Inert High-Modulus Components

Increasing the Efficiency of Blast Technologies and Their Applications

By: Igor A. Balagansky,Anatoliy A. Bataev,Ivan A. Bataev

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Explosion Systems with Inert High-Modulus Components

Increasing the Efficiency of Blast Technologies and Their Applications

By: Igor A. Balagansky,Anatoliy A. Bataev,Ivan A. Bataev

Describes in one volume the data received during experiments on detonation in high explosive charges This book brings together, in one volume, information normally covered in a series of journal articles on high explosive detonation tests, so that developers can create new explosive technologies. It focuses on the charges that contain inert elements made of materials in which a sound velocity is significantly higher than a detonation velocity. It also summarizes the results of experimental, numerical, and theoretical investigations of explosion systems, which contain high modulus ceramic components. The phenomena occurring in such systems are described in detail: desensitization of high explosives, nonstationary detonation processes, energy focusing, and Mach stems formation. Formation of hypersonic flows of ceramic particles arising due to explosive collapse of ceramic tubes is another example of the issues discussed. Explosion Systems with Inert High Modulus Components: Increasing the Efficiency of Blast Technologies and Their Applications also looks at the design of explosion protective structures based on high modulus ceramic materials. The structural transformations, caused in metallic materials by the energy focusing, or by the impact of hypersonic ceramic jets are also discussed. These transformations include, but not limited to adiabatic shear banding, phase transformations, mechanical twinning, melting, boiling, and even evaporation of the impacted substrates. Specifically discusses in one volume the explosions involved with inert high modules components normally scattered over numerous journal articles Covers methods to increase energy output of a weak explosive by encasing it in a higher explosive Discusses the specifics of explosive systems containing high modulus inert elements Details the process of detonation and related phenomena, as well as the design of novel highly performant explosive systems Describes the transformation in materials impacted due to explosion in such systems Explosion Systems with Inert High Modulus Components will be of great interest to specialists working in fields of energy of the explosion and explosion safety as well as university staff, students, and postgraduate students studying explosion phenomena, explosive technologies, explosion safety, and materials science.

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228

Publisher

John Wiley & Sons

Published Date

ISBN 10

111952542X

ISBN 13

9781119525424

The Development of the Bulgarian Literary Language

From Incunabula to First Grammars, Late Fifteenth – Early Seventeenth Century

By: Ivan N. Petrov

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The Development of the Bulgarian Literary Language

From Incunabula to First Grammars, Late Fifteenth – Early Seventeenth Century

By: Ivan N. Petrov

Ivan N. Petrov’s The Development of the Bulgarian Literary Language: From Incunabula to First Grammars, Late Fifteenth–Early Seventeenth Century examines the history of the first printed Cyrillic books and their role in the development of the Bulgarian literary language. In the literary culture of the Southern Slavs, especially the Bulgarians, the period that began at the end of the fifteenth century and covered the sixteenth and seventeenth centuries is often seen as a foreshadowing of the pre-national era of modern times. In particular, the centuries-old manuscript tradition was gradually replaced by the Cyrillic printed book, which—after the incunabula of Krakow and Montenegro—was published in such centers as Târgoviște, Prague, Venice, Serbian monasteries, Vilnius, Moscow, Zabłudów, Lviv, Ostroh, and many others. Petrov shows how the study of old Slavic prints is closely linked to the processes that determined the emergence of modern literary languages in the Slavia Orthodoxa area, including the influence of the liturgical Church Slavonic language shared by the Orthodox Slavs, which was increasingly standardized and codified at that time. The perspective of a language historian brings new light to the complex and multidimensional issues of this important transitional period of Slavic history and culture.

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199

Publisher

Rowman & Littlefield

Published Date

ISBN 10

1498586082

ISBN 13

9781498586085

Drought in Bulgaria

A Contemporary Analog for Climate Change

By: Ivan Raev

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Drought in Bulgaria

A Contemporary Analog for Climate Change

By: Ivan Raev

Given the different geographical and human contexts in which climate change impacts will be experienced, thinking by analogy provides one useful way to explore dimensions of such change. This landmark book, based on the human dimensions of global change paradigm, examines climate impacts through the study of a contemporary society's response to a drought period analogous to expected future climate. Bulgaria suffered severe drought from 1982 to 1994, conditions which models suggest better reflects future climate than existing norms. The Center for Integrated Regional Assessment at Penn State and the Bulgarian Academy of Sciences collaborated to research this drought period as an analogy for future climate. This book shows their assessment of what can be learned from the impacts of and responses to contemporary drought in Bulgaria. It therefore makes an important contribution to the literature on regional climate change and methods used to address anticipated impacts of that change.

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453

Publisher

Routledge

Published Date

ISBN 10

135115950X

ISBN 13

9781351159500

Fathers and Sons

By: Ivan Turgenev

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Fathers and Sons

By: Ivan Turgenev

Turgenev's masterpiece about the conflict between generations is as fresh, outspoken, and exciting today as it was in when it was first published in 1862. The controversial portrait of Bazarov, the energetic, cynical, and self-assured `nihilist' who repudiates the romanticism of his elders, shook Russian society. Indeed the image of humanity liberated by science from age-old conformities and prejudices is one that can threaten establishments of any political or religious persuasion, and is especially potent in the modern era. This new translation, specially commissioned for the World's Classics, is the first to draw on Turgenev's working manuscript, which only came to light in 1988. ABOUT THE SERIES: For over 100 years Oxford World's Classics has made available the widest range of literature from around the globe. Each affordable volume reflects Oxford's commitment to scholarship, providing the most accurate text plus a wealth of other valuable features, including expert introductions by leading authorities, helpful notes to clarify the text, up-to-date bibliographies for further study, and much more.

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402

Publisher

OUP Oxford

Published Date

ISBN 10

019160545X

ISBN 13

9780191605451

Advanced Deep Learning with Python

Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

By: Ivan Vasilev

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Advanced Deep Learning with Python

Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

By: Ivan Vasilev

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456

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Packt Publishing Ltd

Published Date

ISBN 10

1789952719

ISBN 13

9781789952711

Python Deep Learning

Understand how deep neural networks work and apply them to real-world tasks

By: Ivan Vasilev

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Python Deep Learning

Understand how deep neural networks work and apply them to real-world tasks

By: Ivan Vasilev

Master effective navigation of neural networks, including convolutions and transformers, to tackle computer vision and NLP tasks using Python Key Features Understand the theory, mathematical foundations and structure of deep neural networks Become familiar with transformers, large language models, and convolutional networks Learn how to apply them to various computer vision and natural language processing problems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe field of deep learning has developed rapidly recently and today covers a broad range of applications. This makes it challenging to navigate and hard to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today. The first part of the book introduces the main machine learning concepts and paradigms. It covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning. The second part of the book introduces convolutional networks for computer vision. We’ll learn how to solve image classification, object detection, instance segmentation, and image generation tasks. The third part focuses on the attention mechanism and transformers – the core network architecture of large language models. We’ll discuss new types of advanced tasks they can solve, such as chatbots and text-to-image generation. By the end of this book, you’ll have a thorough understanding of the inner workings of deep neural networks. You'll have the ability to develop new models and adapt existing ones to solve your tasks. You’ll also have sufficient understanding to continue your research and stay up to date with the latest advancements in the field.What you will learn Establish theoretical foundations of deep neural networks Understand convolutional networks and apply them in computer vision applications Become well versed with natural language processing and recurrent networks Explore the attention mechanism and transformers Apply transformers and large language models for natural language and computer vision Implement coding examples with PyTorch, Keras, and Hugging Face Transformers Use MLOps to develop and deploy neural network models Who this book is for This book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians, and anyone interested in deep learning. Prior experience with Python programming is a prerequisite.

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362

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Packt Publishing Ltd

Published Date

ISBN 10

1837633452

ISBN 13

9781837633456

Python Deep Learning

Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow

By: Ivan Vasilev,Daniel Slater,Gianmario Spacagna,Peter Roelants,Valentino Zocca

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Python Deep Learning

Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow

By: Ivan Vasilev,Daniel Slater,Gianmario Spacagna,Peter Roelants,Valentino Zocca

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379

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Packt Publishing Ltd

Published Date

ISBN 10

1789349702

ISBN 13

9781789349702

Gas Hydrate

Environmental and Climate Impacts

By: Umberta Tinivella,Michela Giustiniani,Ivan De La Cruz Vargas Cordero,Atanas Vasilev

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Gas Hydrate

Environmental and Climate Impacts

By: Umberta Tinivella,Michela Giustiniani,Ivan De La Cruz Vargas Cordero,Atanas Vasilev

This Special Issue reports research spanning from the analysis of indirect data, modeling, and laboratory and geological data confirming the intrinsic multidisciplinarity of gas hydrate studies. The study areas are (1) Arctic, (2) Brazil, (3) Chile, and (4) the Mediterranean region. The results furnished an important tessera of the knowledge about the relationship of a gas hydrate system with other complex natural phenomena such as climate change, slope stability and earthquakes, and human activities.

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182

Publisher

MDPI

Published Date

ISBN 10

3039218441

ISBN 13

9783039218448

Book's by Ivan Vasilev

Python Deep Learning

Python Deep Learning

Advanced Deep Learning with Python

Advanced Deep Learning with Python

Explosion Systems with Inert High-Modulus Components

Explosion Systems with Inert High-Modulus Components

The Development of the Bulgarian Literary Language

The Development of the Bulgarian Literary Language

Drought in Bulgaria

Drought in Bulgaria

Fathers and Sons

Fathers and Sons

Advanced Deep Learning with Python

Advanced Deep Learning with Python

Python Deep Learning

Python Deep Learning

Python Deep Learning

Python Deep Learning

Gas Hydrate

Gas Hydrate

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