Adversarial Networks

Adversarial Networks Generative
Adversarial Networks 電子書籍
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商品	説明	価格
Hands-On Generative Adversarial Networks with PyTorch 1.x Implement next-generation neural networks to build powerful GAN models using Python【電子書籍】[ John Hany ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Apply deep learning techniques and neural network methodologies to build, train, and optimize generative network models＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Implement GAN architectures to generate images, text, audio, 3D models, and more＜/li＞＜li＞Understand how GANs work and become an active contributor in the open source community＜/li＞＜li＞Learn how to generate photo-realistic images based on text descriptions＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞With continuously evolving research and development, Generative Adversarial Networks (GANs) are the next big thing in the field of deep learning. This book highlights the key improvements in GANs over generative models and guides in making the best out of GANs with the help of hands-on examples.＜/p＞＜p＞This book starts by taking you through the core concepts necessary to understand how each component of a GAN model works. You'll build your first GAN model to understand how generator and discriminator networks function. As you advance, you'll delve into a range of examples and datasets to build a variety of GAN networks using PyTorch functionalities and services, and become well-versed with architectures, training strategies, and evaluation methods for image generation, translation, and restoration. You'll even learn how to apply GAN models to solve problems in areas such as computer vision, multimedia, 3D models, and natural language processing (NLP). The book covers how to overcome the challenges faced while building generative models from scratch. Finally, you'll also discover how to train your GAN models to generate adversarial examples to attack other CNN and GAN models.＜/p＞＜p＞By the end of this book, you will have learned how to build, train, and optimize next-generation GAN models and use them to solve a variety of real-world problems.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Implement PyTorch's latest features to ensure efficient model designing＜/li＞＜li＞Get to grips with the working mechanisms of GAN models＜/li＞＜li＞Perform style transfer between unpaired image collections with CycleGAN＜/li＞＜li＞Build and train 3D-GANs to generate a point cloud of 3D objects＜/li＞＜li＞Create a range of GAN models to perform various image synthesis operations＜/li＞＜li＞Use SEGAN to suppress noise and improve the quality of speech audio＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞This GAN book is for machine learning practitioners and deep learning researchers looking to get hands-on guidance in implementing GAN models using PyTorch. You'll become familiar with state-of-the-art GAN architectures with the help of real-world examples. Working knowledge of Python programming language is necessary to grasp the concepts covered in this book.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	3,290円
Generative Adversarial Networks for Image-to-Image Translation【電子書籍】楽天Kobo電子書籍ストア	＜p＞Generative Adversarial Networks (GAN) have started a revolution in Deep Learning, and today GAN is one of the most researched topics in Artificial Intelligence. ＜em＞Generative Adversarial Networks for Image-to-Image Translation＜/em＞ provides a comprehensive overview of the GAN (Generative Adversarial Network) concept starting from the original GAN network to various GAN-based systems such as Deep Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN, Wasserstein GANs (WGAN), cyclical GANs, and many more. The book also provides readers with detailed real-world applications and common projects built using the GAN system with respective Python code. A typical GAN system consists of two neural networks, i.e., generator and discriminator. Both of these networks contest with each other, similar to game theory. The generator is responsible for generating quality images that should resemble ground truth, and the discriminator is accountable for identifying whether the generated image is a real image or a fake image generated by the generator. Being one of the unsupervised learning-based architectures, GAN is a preferred method in cases where labeled data is not available. GAN can generate high-quality images, images of human faces developed from several sketches, convert images from one domain to another, enhance images, combine an image with the style of another image, change the appearance of a human face image to show the effects in the progression of aging, generate images from text, and many more applications. GAN is helpful in generating output very close to the output generated by humans in a fraction of second, and it can efficiently produce high-quality music, speech, and images.＜/p＞＜ul＞＜li＞Introduces the concept of Generative Adversarial Networks (GAN), including the basics of Generative Modelling, Deep Learning, Autoencoders, and advanced topics in GAN＜/li＞＜li＞Demonstrates GANs for a wide variety of applications, including image generation, Big Data and data analytics, cloud computing, digital transformation, E-Commerce, and Artistic Neural Networks＜/li＞＜li＞Includes a wide variety of biomedical and scientific applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing, and disease diagnosis＜/li＞＜li＞Provides a robust set of methods that will help readers to appropriately and judiciously use the suitable GANs for their applications＜/li＞＜/ul＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	16,807円
Generative Adversarial Networks with Industrial Use Cases: Learning How to Build GAN Applications for Retail, Healthcare, Telecom, Media, Education, and HRTech【電子書籍】[ Navin K. Manaswi (Google Developer Expert) ] 楽天Kobo電子書籍ストア	＜p＞Best Book on GAN＜/p＞＜p＞This book aims at simplifying GAN for everyone. This book is very important for machine learning engineers, researchers, students, professors, and professionals. Universities and online course instructors will find this book very interesting for teaching advanced deep learning, specially Generative Adversarial Networks(GAN). Industry professionals, coders, and data scientists can learn GAN from scratch. They can learn how to build GAN codes for industrial applications for Healthcare, Retail, HRTech, EduTech, Telecom, Media, and Entertainment. Mathematics of GAN is discussed and illustrated. KL divergence and other parts of GAN are illustrated and discussed mathematically. This book teaches how to build codes for pix2pix GAN, DCGAN, CGAN, styleGAN, cycleGAN, and many other GAN. Machine Learning and Deep Learning Researchers will learn GAN in the shortest possible time with the help of this book.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	1,100円
Generative Adversarial Networks for Image Generation【電子書籍】[ Xudong Mao ] 楽天Kobo電子書籍ストア	＜p＞Generative adversarial networks (GANs) were introduced by Ian Goodfellow and his co-authors including Yoshua Bengio in 2014, and were to referred by Yann Lecun (Facebook’s AI research director) as “the most interesting idea in the last 10 years in ML.” GANs’ potential is huge, because they can learn to mimic any distribution of data, which means they can be taught to create worlds similar to our own in any domain: images, music, speech, prose. They are robot artists in a sense, and their output is remarkable ? poignant even. In 2018, Christie’s sold a portrait that had been generated by a GAN for $432,000.＜/p＞＜p＞Although image generation has been challenging, GAN image generation has proved to be very successful and impressive. However, there are two remaining challenges for GAN image generation: the quality of the generated image and the training stability. This book first provides an overview of GANs, and then discusses the task of image generation and the detailsof GAN image generation. It also investigates a number of approaches to address the two remaining challenges for GAN image generation. Additionally, it explores three promising applications of GANs, including image-to-image translation, unsupervised domain adaptation and GANs for security. This book appeals to students and researchers who are interested in GANs, image generation and general machine learning and computer vision.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	18,231円
洋書 Paperback, GANs in Action: Deep learning with Generative Adversarial Networks Glomarket	* We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 * 特に注意してください。 * ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。・お名前またはご住所が法人・団体名義（XX株式会社等）、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 * ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。・当店でご購入された商品は、原則として、「個人輸入」としての取り扱いになり、すべてニュージャージからお客様のもとへ直送されます。・ご注文後、30営業日以内(通常2~3週間)に配送手続きをいたします。配送作業完了後、2週間程度でのお届けとなります。・まれに商品入荷状況や国際情勢、運送、通関事情により、お届けが2ヶ月までかかる場合がありますのでお急ぎの場合は注文をお控えください。・個人輸入される商品は、すべてご注文者自身の「個人使用・個人消費」が前提となりますので、ご注文された商品を第三者へ譲渡・転売することは法律で禁止されております。・関税・消費税が課税される場合があります。詳細はこちらをご確認下さい。PC販売説明文	10,824円
Generative Adversarial Networks Cookbook Over 100 recipes to build generative models using Python, TensorFlow, and Keras【電子書籍】[ Josh Kalin ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Simplify next-generation deep learning by implementing powerful generative models using Python, TensorFlow and Keras＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Understand the common architecture of different types of GANs＜/li＞＜li＞Train, optimize, and deploy GAN applications using TensorFlow and Keras＜/li＞＜li＞Build generative models with real-world data sets, including 2D and 3D data＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞Developing Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand.＜/p＞＜p＞This book leads you through eight different examples of modern GAN implementations, including CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation. Each chapter contains useful recipes to build on a common architecture in Python, TensorFlow and Keras to explore increasingly difficult GAN architectures in an easy-to-read format. The book starts by covering the different types of GAN architecture to help you understand how the model works. This book also contains intuitive recipes to help you work with use cases involving DCGAN, Pix2Pix, and so on. To understand these complex applications, you will take different real-world data sets and put them to use.＜/p＞＜p＞By the end of this book, you will be equipped to deal with the challenges and issues that you may face while working with GAN models, thanks to easy-to-follow code solutions that you can implement right away.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Structure a GAN architecture in pseudocode＜/li＞＜li＞Understand the common architecture for each of the GAN models you will build＜/li＞＜li＞Implement different GAN architectures in TensorFlow and Keras＜/li＞＜li＞Use different datasets to enable neural network functionality in GAN models＜/li＞＜li＞Combine different GAN models and learn how to fine-tune them＜/li＞＜li＞Produce a model that can take 2D images and produce 3D models＜/li＞＜li＞Develop a GAN to do style transfer with Pix2Pix＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞This book is for data scientists, machine learning developers, and deep learning practitioners looking for a quick reference to tackle challenges and tasks in the GAN domain. Familiarity with machine learning concepts and working knowledge of Python programming language will help you get the most out of the book.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	4,085円
Generative Adversarial Networks in Practice【電子書籍】[ Mehdi Ghayoumi ] 楽天Kobo電子書籍ストア	＜p＞This book is an all-inclusive resource that provides a solid foundation on Generative Adversarial Networks (GAN) methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts.＜/p＞＜p＞＜strong＞Key Features:＜/strong＞＜/p＞＜ul＞＜li＞＜strong＞G＜/strong＞uides you through the complex world of GANs, demystifying their intricacies＜/li＞＜li＞＜strong＞A＜/strong＞ccompanies your learning journey with real-world examples and practical applications＜/li＞＜li＞＜strong＞N＜/strong＞avigates the theory behind GANs, presenting it in an accessible and comprehensive way＜/li＞＜li＞＜strong＞S＜/strong＞implifies the implementation of GANs using popular deep learning platforms＜/li＞＜li＞＜strong＞I＜/strong＞ntroduces various GAN architectures, giving readers a broad view of their applications＜/li＞＜li＞＜strong＞N＜/strong＞urture your knowledge of AI with our comprehensive yet accessible content＜/li＞＜li＞＜strong＞P＜/strong＞ractice your skills with numerous case studies and coding examples＜/li＞＜li＞＜strong＞R＜/strong＞eviews advanced GANs, such as DCGAN, cGAN, and CycleGAN, with clear explanations and practical examples＜/li＞＜li＞＜strong＞A＜/strong＞dapts to both beginners and experienced practitioners, with content organized to cater to varying levels of familiarity with GANs＜/li＞＜li＞＜strong＞C＜/strong＞onnects the dots between GAN theory and practice, providing a well-rounded understanding of the subject＜/li＞＜li＞＜strong＞T＜/strong＞akes you through GAN applications across different data types, highlighting their versatility＜/li＞＜li＞＜strong＞I＜/strong＞nspires the reader to explore beyond this book, fostering an environment conducive to independent learning and research＜/li＞＜li＞＜strong＞C＜/strong＞loses the gap between complex GAN methodologies and their practical implementation, allowing readers to directly apply their knowledge＜/li＞＜li＞＜strong＞E＜/strong＞mpowers you with the skills and knowledge needed to confidently use GANs in your projects＜/li＞＜/ul＞＜p＞Prepare to deep dive into the captivating realm of GANs and experience the power of AI like never before with Generative Adversarial Networks (GANs) in Practice. This book brings together the theory and practical aspects of GANs in a cohesive and accessible manner, making it an essential resource for both beginners and experienced practitioners.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	13,170円
洋書 Paperback, Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery Glomarket	* We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 * 特に注意してください。 * ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。・お名前またはご住所が法人・団体名義（XX株式会社等）、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 * ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。・当店でご購入された商品は、原則として、「個人輸入」としての取り扱いになり、すべてニュージャージからお客様のもとへ直送されます。・ご注文後、30営業日以内(通常2~3週間)に配送手続きをいたします。配送作業完了後、2週間程度でのお届けとなります。・まれに商品入荷状況や国際情勢、運送、通関事情により、お届けが2ヶ月までかかる場合がありますのでお急ぎの場合は注文をお控えください。・個人輸入される商品は、すべてご注文者自身の「個人使用・個人消費」が前提となりますので、ご注文された商品を第三者へ譲渡・転売することは法律で禁止されております。・関税・消費税が課税される場合があります。詳細はこちらをご確認下さい。	9,164円
Generative Adversarial Networks and Deep Learning Theory and Applications【電子書籍】楽天Kobo電子書籍ストア	＜p＞This book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks, which includes creating new tools and methods for processing text, images, and audio.＜/p＞＜p＞A Generative Adversarial Network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications. Generative Adversarial Networks(GANs) have the feasibility to build improved models, as they can generate the sample data as per application requirements. There are various applications of GAN in science and technology, including computer vision, security, multimedia and advertisements, image generation, image translation,text-to-images synthesis, video synthesis, generating high-resolution images, drug discovery, etc.＜/p＞＜p＞＜em＞＜strong＞Features＜/strong＞＜/em＞:＜/p＞＜ul＞＜li＞Presents a comprehensive guide on how to use GAN for images and videos.＜/li＞＜li＞Includes case studies of Underwater Image Enhancement Using Generative Adversarial Network, Intrusion detection using GAN＜/li＞＜li＞Highlights the inclusion of gaming effects using deep learning methods＜/li＞＜li＞Examines the significant technological advancements in GAN and its real-world application.＜/li＞＜li＞Discusses as GAN challenges and optimal solutions＜/li＞＜/ul＞＜p＞The book addresses scientific aspects for a wider audience such as junior and senior engineering, undergraduate and postgraduate students, researchers, and anyone interested in the trends development and opportunities in GAN and Deep Learning.＜/p＞＜p＞The material in the book can serve as a reference in libraries, accreditation agencies, government agencies, and especially the academic institution of higher education intending to launch or reform their engineering curriculum＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	8,604円
Generating a New Reality From Autoencoders and Adversarial Networks to Deepfakes【電子書籍】[ Micheal Lanham ] 楽天Kobo電子書籍ストア	＜p＞The emergence of artificial intelligence (AI) has brought us to the precipice of a new age where we struggle to understand what is real, from advanced CGI in movies to even faking the news. AI that was developed to understand our reality is now being used to create its own reality.＜/p＞＜p＞In this book we look at the many AI techniques capable of generating new realities. We start with the basics of deep learning. Then we move on to autoencoders and generative adversarial networks (GANs). We explore variations of GAN to generate content. The book ends with an in-depth look at the most popular generator projects.＜/p＞＜p＞By the end of this book you will understand the AI techniques used to generate different forms of content. You will be able to use these techniques for your own amusement or professional career to both impress and educate others around you and give you the ability to transform your own reality into something new.＜/p＞＜p＞＜strong＞What You Will Learn＜/strong＞＜/p＞＜ul＞＜li＞Know the fundamentals of content generation from autoencoders to generative adversarial networks (GANs)＜/li＞＜li＞Explore variations of GAN＜/li＞＜li＞Understand the basics of other forms of content generation＜/li＞＜li＞Use advanced projects such as Faceswap, deepfakes, DeOldify, and StyleGAN2＜/li＞＜/ul＞＜p＞＜strong＞Who This Book Is For＜/strong＞＜/p＞＜p＞Machine learning developers and AI enthusiasts who want to understand AI content generation techniques＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	7,900円
洋書 Paperback, Hands-On Generative Adversarial Networks with PyTorch 1.x: Implement next-generation neural networks to build powerful GAN models using Python Glomarket	* We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 * 特に注意してください。 * ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。・お名前またはご住所が法人・団体名義（XX株式会社等）、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 * ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。・当店でご購入された商品は、原則として、「個人輸入」としての取り扱いになり、すべてニュージャージからお客様のもとへ直送されます。・ご注文後、30営業日以内(通常2~3週間)に配送手続きをいたします。配送作業完了後、2週間程度でのお届けとなります。・まれに商品入荷状況や国際情勢、運送、通関事情により、お届けが2ヶ月までかかる場合がありますのでお急ぎの場合は注文をお控えください。・個人輸入される商品は、すべてご注文者自身の「個人使用・個人消費」が前提となりますので、ご注文された商品を第三者へ譲渡・転売することは法律で禁止されております。・関税・消費税が課税される場合があります。詳細はこちらをご確認下さい。PC販売説明文	9,925円
【中古】【未使用・未開封品】Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras AJIMURA-SHOP	【中古】【未使用・未開封品】Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras【メーカー名】【メーカー型番】【ブランド名】Packt Publishing Human Vision & Language Systems, Machine Learning, Machine Vision, Neural Networks, Theory of Computing, Paperback Store, Amazon Student ポイント還元(洋書), Amazonアプリキャンペーン対象商品(洋書), 洋書（アダルト除く） Ahirwar, Kailash: Author【商品説明】Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras【注意】こちらは輸入品となります。当店では初期不良に限り、商品到着から7日間は返品を受付けております。こちらは当店海外ショップで一般の方から買取した未使用・未開封品です。買取した為、中古扱いとしております。他モールとの併売品の為、完売の際はご連絡致しますのでご了承ください。ご注文からお届けまで1、ご注文⇒ご注文は24時間受け付けております。2、注文確認⇒ご注文後、当店から注文確認メールを送信します。3、当店海外倉庫から当店日本倉庫を経由しお届けしますので10〜30営業日程度でのお届けとなります。4、入金確認⇒前払い決済をご選択の場合、ご入金確認後、配送手配を致します。5、出荷⇒配送準備が整い次第、出荷致します。配送業者、追跡番号等の詳細をメール送信致します。6、到着⇒出荷後、1〜3日後に商品が到着します。　※離島、北海道、九州、沖縄は遅れる場合がございます。予めご了承下さい。お電話でのお問合せは少人数で運営の為受け付けておりませんので、メールにてお問合せお願い致します。営業時間　月〜金　10:00〜17:00お客様都合によるご注文後のキャンセル・返品はお受けしておりませんのでご了承下さい。	21,514円
洋書 Paperback, Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras Glomarket	* We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 * 特に注意してください。 * ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。・お名前またはご住所が法人・団体名義（XX株式会社等）、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 * ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。・当店でご購入された商品は、原則として、「個人輸入」としての取り扱いになり、すべてニュージャージからお客様のもとへ直送されます。・ご注文後、30営業日以内(通常2~3週間)に配送手続きをいたします。配送作業完了後、2週間程度でのお届けとなります。・まれに商品入荷状況や国際情勢、運送、通関事情により、お届けが2ヶ月までかかる場合がありますのでお急ぎの場合は注文をお控えください。・個人輸入される商品は、すべてご注文者自身の「個人使用・個人消費」が前提となりますので、ご注文された商品を第三者へ譲渡・転売することは法律で禁止されております。・関税・消費税が課税される場合があります。詳細はこちらをご確認下さい。PC販売説明文	10,750円
Generative Adversarial Networks with Industrial Use Cases Learning How to Build GAN Applications for Retail, Healthcare, Telecom, Media, Education, and HRTech【電子書籍】[ Navin K Manaswi ] 楽天Kobo電子書籍ストア	＜p＞This book aims at simplifying GAN for everyone. This book is very important for machine learning engineers, researchers, students, professors, and professionals. Universities and online course instructors will find this book very interesting for teaching advanced deep learning, specially Generative Adversarial Networks(GAN). Industry professionals, coders, and data scientists can learn GAN from scratch. They can learn how to build GAN codes for industrial applications for Healthcare, Retail, HRTech, EduTech, Telecom, Media, and Entertainment. Mathematics of GAN is discussed and illustrated. KL divergence and other parts of GAN are illustrated and discussed mathematically. This book teaches how to build codes for pix2pix GAN, DCGAN, CGAN, styleGAN, cycleGAN, and many other GAN. Machine Learning and Deep Learning Researchers will learn GAN in the shortest possible time with the help of this book.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	1,597円
A Primer on Generative Adversarial Networks【電子書籍】[ Sanaa Kaddoura ] 楽天Kobo電子書籍ストア	＜p＞This book is meant for readers who want to understand GANs without the need for a strong mathematical background. Moreover, it covers the practical applications of GANs, making it an excellent resource for beginners. ＜em＞A Primer on Generative Adversarial Networks＜/em＞ is suitable for researchers, developers, students, and anyone who wishes to learn about GANs. It is assumed that the reader has a basic understanding of machine learning and neural networks. The book comes with ready-to-run scripts that readers can use for further research. Python is used as the primary programming language, so readers should be familiar with its basics.＜/p＞＜p＞The book starts by providing an overview of GAN architecture, explaining the concept of generative models. It then introduces the most straightforward GAN architecture, which explains how GANs work and covers the concepts of generator and discriminator. The book then goes into the more advanced real-world applications of GANs, such as human face generation, deep fake, CycleGANs, and more.＜/p＞＜p＞By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	5,469円
GANs mit PyTorch selbst programmieren Ein verst?ndlicher Einstieg in Generative Adversarial Networks【電子書籍】[ Tariq Rashid ] 楽天Kobo電子書籍ストア	＜h2＞Neues von Bestsellerautor Tariq Rashid: Eine Einf?hrung in die innovative Deep-Learning-Technik GANs＜/h2＞＜ul＞＜li＞Schritt-f?r-Schritt-Anleitung zum Erstellen eigener GANs mit PyTorch, regt zum Ausprobieren an＜/li＞＜li＞GANs (Generative Adversarial Networks) geh?ren zu den spannendsten neuen Algorithmen im Machine Learning＜/li＞＜li＞Tariq Rashid erkl?rt diese schwierige Materie au?ergew?hnlich klar und gut nachvollziehbar＜/li＞＜/ul＞＜p＞"Die coolste Idee im Deep Learning in den letzten 20 Jahren" sagt Yann LeCun, einer der weltweit f?hrenden Forscher auf dem Gebiet der neuronalen Netze, ?ber GANs, die Generative Adversarial Networks. Bei dieser noch neuen KI-Technik treten zwei neuronale Netze gegeneinander an mit dem Ziel, Bilder, Ton und Videos zu erzeugen, die vom Original nicht zu unterscheiden sind.＜br /＞ Dieses Buch richtet sich an alle, die selbst ausprobieren m?chten, wie GANs funktionieren. Tariq Rashid zeigt Ihnen Schritt f?r Schritt, wie Sie mit dem popul?ren Framework PyTorch Ihre eigenen GANs erstellen und trainieren. Sie starten mit einem sehr einfachen GAN, um einen Workflow einzurichten, und ?ben erste Techniken anhand der MNIST-Datenbank ein. Mit diesem Wissen programmieren Sie dann ein GAN, das realistische menschliche Gesichter erzeugen kann. Tariq Rashids besondere F?higkeit, komplexe Ideen verst?ndlich zu erkl?ren, macht das Buch zu einer unterhaltsamen Lekt?re.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	5,400円
Strengthening Deep Neural Networks Making AI Less Susceptible to Adversarial Trickery【電子書籍】[ Katy Warr ] 楽天Kobo電子書籍ストア	＜p＞As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNsーthe algorithms intrinsic to much of AIーare used daily to process image, audio, and video data.＜/p＞＜p＞Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you.＜/p＞＜ul＞＜li＞Delve into DNNs and discover how they could be tricked by adversarial input＜/li＞＜li＞Investigate methods used to generate adversarial input capable of fooling DNNs＜/li＞＜li＞Explore real-world scenarios and model the adversarial threat＜/li＞＜li＞Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data＜/li＞＜li＞Examine some ways in which AI might become better at mimicking human perception in years to come＜/li＞＜/ul＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	5,430円
Generative Adversarial Networks Projects Build next-generation generative models using TensorFlow and Keras【電子書籍】[ Kailash Ahirwar ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Explore various Generative Adversarial Network architectures using the Python ecosystem＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Use different datasets to build advanced projects in the Generative Adversarial Network domain＜/li＞＜li＞Implement projects ranging from generating 3D shapes to a face aging application＜/li＞＜li＞Explore the power of GANs to contribute in open source research and projects＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain.＜/p＞＜p＞Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you'll gain an understanding of the architecture and functioning of generative models through their practical implementation.＜/p＞＜p＞By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Train a network on the 3D ShapeNet dataset to generate realistic shapes＜/li＞＜li＞Generate anime characters using the Keras implementation of DCGAN＜/li＞＜li＞Implement an SRGAN network to generate high-resolution images＜/li＞＜li＞Train Age-cGAN on Wiki-Cropped images to improve face verification＜/li＞＜li＞Use Conditional GANs for image-to-image translation＜/li＞＜li＞Understand the generator and discriminator implementations of StackGAN in Keras＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞If you're a data scientist, machine learning developer, deep learning practitioner, or AI enthusiast looking for a project guide to test your knowledge and expertise in building real-world GANs models, this book is for you.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	3,858円
Hands-On Generative Adversarial Networks with Keras Your guide to implementing next-generation generative adversarial networks【電子書籍】[ Rafael Valle ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Develop generative models for a variety of real-world use-cases and deploy them to production＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Discover various GAN architectures using Python and Keras library＜/li＞＜li＞Understand how GAN models function with the help of theoretical and practical examples＜/li＞＜li＞Apply your learnings to become an active contributor to open source GAN applications＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞Generative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning. This book will be your first step towards understanding GAN architectures and tackling the challenges involved in training them.＜/p＞＜p＞This book opens with an introduction to deep learning and generative models, and their applications in artificial intelligence (AI). You will then learn how to build, evaluate, and improve your first GAN with the help of easy-to-follow examples. The next few chapters will guide you through training a GAN model to produce and improve high-resolution images. You will also learn how to implement conditional GANs that give you the ability to control characteristics of GAN outputs. You will build on your knowledge further by exploring a new training methodology for progressive growing of GANs. Moving on, you'll gain insights into state-of-the-art models in image synthesis, speech enhancement, and natural language generation using GANs. In addition to this, you'll be able to identify GAN samples with TequilaGAN.＜/p＞＜p＞By the end of this book, you will be well-versed with the latest advancements in the GAN framework using various examples and datasets, and you will have the skills you need to implement GAN architectures for several tasks and domains, including computer vision, natural language processing (NLP), and audio processing.＜/p＞＜p＞Foreword by Ting-Chun Wang, Senior Research Scientist, NVIDIA＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Learn how GANs work and the advantages and challenges of working with them＜/li＞＜li＞Control the output of GANs with the help of conditional GANs, using embedding and space manipulation＜/li＞＜li＞Apply GANs to computer vision, NLP, and audio processing＜/li＞＜li＞Understand how to implement progressive growing of GANs＜/li＞＜li＞Use GANs for image synthesis and speech enhancement＜/li＞＜li＞Explore the future of GANs in visual and sonic arts＜/li＞＜li＞Implement pix2pixHD to turn semantic label maps into photorealistic images＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking for a perfect mix of theory and hands-on content in order to implement GANs using Keras. Working knowledge of Python is expected.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	3,290円
The GAN Book: Train stable Generative Adversarial Networks using TensorFlow2, Keras and Python【電子書籍】[ Kartik Chaudhary ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Key Features＜/strong＞＜/p＞＜p＞- Learn generative learning approach of ML and its key differences from the discriminative learning approach.＜/p＞＜p＞- Understand why GANs are difficult to train, and key techniques to make their training stable to get impressive results.＜/p＞＜p＞- Implement multiple variants of GANs for solving problems such as image generation, image-to-image translation, image super- resolution and so on.＜/p＞＜p＞＜strong＞Book Description＜/strong＞＜/p＞＜p＞Generative Adversarial Networks have become quite popular due to their wide variety of applications in the fields of Computer Vision, Digital Marketing, Creative artwork and so on. One key challenge with GANs is that they are very difficult to train.＜/p＞＜p＞This book is a comprehensive guide that highlights the common challenges of training GANs and also provides guidelines for developing GANs in such a way that they result in stable training and high-quality results. This book also explains the generative learning approach of training ML models and its key differences from the discriminative learning approach. After covering the different generative learning approaches, this book deeps dive more into the Generative Adversarial Network and their key variants.＜/p＞＜p＞This book takes a hands-on approach and implements multiple generative models such as Pixel CNN, VAE, GAN, DCGAN, CGAN, SGAN, InfoGAN, ACGAN, WGAN, LSGAN, WGAN-GP, Pix2Pix, CycleGAN, SRGAN, DiscoGAN, CartoonGAN, Context Encoder and so on. It also provides a detailed explanation of some advanced GAN variants such as BigGAN, PGGAN, StyleGAN and so on. This book will make you a GAN champion in no time.＜/p＞＜p＞What will you learn＜/p＞＜p＞- Learn about the generative learning approach of training ML models＜/p＞＜p＞- Understand key differences of the generative learning approach from the discriminative learning approach＜/p＞＜p＞- Learn about various generative learning approaches and key technical aspects behind them＜/p＞＜p＞- Understand and implement the Generative Adversarial Networks in details＜/p＞＜p＞- Learn about some key challenges faced during GAN training and two common training failure modes＜/p＞＜p＞- Build expertise in the best practices and guidelines for developing and training stable GANs＜/p＞＜p＞- Implement multiple variants of GANs and verify their results on your own datasets＜/p＞＜p＞- Learn about the adversarial examples, some key applications of GANs and common evaluation strategies＜/p＞＜p＞＜strong＞Who this book is for＜/strong＞＜/p＞＜p＞If you are a ML practitioner who wants to learn about generative learning approaches and get expertise in Generative Adversarial Networks for generating high-quality and realistic content, this book is for you. Starting from a gentle introduction to the generative learning approaches, this book takes you through different variants of GANs, explaining some key technical and intuitive aspects about them. This book provides hands-on examples of multiple GAN variants and also, explains different ways to evaluate them. It covers key applications of GANs and also, explains the adversarial examples.＜/p＞＜p＞＜strong＞Table of Contents＜/strong＞＜/p＞＜ol＞＜li＞＜p＞Generative Learning＜/p＞＜/li＞＜li＞＜p＞Generative Adversarial Networks＜/p＞＜/li＞＜li＞＜p＞GAN Failure Modes＜/p＞＜/li＞＜li＞＜p＞Deep Convolutional GANs＜/p＞＜/li＞＜/ol＞＜p＞4(II). Into the Latent Space＜/p＞＜ol start="5"＞＜li＞＜p＞Towards stable GANs＜/p＞＜/li＞＜li＞＜p＞Conditional GANs＜/p＞＜/li＞＜li＞＜p＞Better Loss functions＜/p＞＜/li＞＜li＞＜p＞Image-to-Image Translation＜/p＞＜/li＞＜li＞＜p＞Other GANs and experiments＜/p＞＜/li＞＜/ol＞＜p＞9(II). Advanced Scaling of GANs＜/p＞＜ol start="10"＞＜li＞＜p＞How to evaluate GANs?＜/p＞＜/li＞＜li＞＜p＞Adversarial Examples＜/p＞＜/li＞＜li＞＜p＞Impressive Applications of GANs＜/p＞＜/li＞＜li＞＜p＞Top Research Papers＜/p＞＜/li＞＜/ol＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	1,550円
洋書 Paperback, Generative Adversarial Networks with Industrial Use Cases: Learning How to Build GAN Applications for Retail, Healthcare, Telecom, Media, Education, and HRTech (English Edition) Glomarket	* We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 * 特に注意してください。 * ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。・お名前またはご住所が法人・団体名義（XX株式会社等）、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 * ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。・当店でご購入された商品は、原則として、「個人輸入」としての取り扱いになり、すべてニュージャージからお客様のもとへ直送されます。・ご注文後、30営業日以内(通常2~3週間)に配送手続きをいたします。配送作業完了後、2週間程度でのお届けとなります。・まれに商品入荷状況や国際情勢、運送、通関事情により、お届けが2ヶ月までかかる場合がありますのでお急ぎの場合は注文をお控えください。・個人輸入される商品は、すべてご注文者自身の「個人使用・個人消費」が前提となりますので、ご注文された商品を第三者へ譲渡・転売することは法律で禁止されております。・関税・消費税が課税される場合があります。詳細はこちらをご確認下さい。PC販売説明文	3,906円