Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch
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Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch
Master key deep learning concepts and different applications of deep learning models in the real world
Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch
товар №: 81061201

Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

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товар №: 81061201

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Shop Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch online at a best price in Kazakhstan. 178995617X
  • Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystemKey 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 DescriptionIn 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 forThis 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.Table of ContentsThe Nuts and Bolts of Neural NetworksUnderstanding Convolutional NetworksAdvanced Convolutional NetworksObject Detection and Image SegmentationGenerative ModelsLanguage ModellingUnderstanding Recurrent NetworksSequence-to-Sequence Models and AttentionEmerging Neural Network DesignsMeta LearningDeep Learning for Autonomous Vehicles
Publisher Packt Publishing
Publication date December 12, 2019
Language English
Print length 468 pages
ISBN-10 178995617X
ISBN-13 978-1789956177
Item Weight 1.76 pounds (800 grams)
Dimensions 7.5 x 1.06 x 9.25 inches (19.1 x 2.7 x 23.5 cm)

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Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

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Вопросы и ответы клиентов

  • вопрос: What is 'Advanced Deep Learning with Python' about?

    отвечать: This book dives deep into complex deep learning concepts using Python as the primary programming language. It covers advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and practical implementations of deep learning algorithms. By exploring real-world applications, readers gain insight into how to tackle challenging problems in areas like image recognition, natural language processing, and more.
  • вопрос: Who is the target audience for this book?

    отвечать: The book is primarily aimed at data scientists, machine learning engineers, and developers with a foundational understanding of Python and machine learning. It’s particularly beneficial for those looking to enhance their skill set in deep learning frameworks and techniques, providing insights that can be directly applied in professional or academic settings, as well as for enthusiasts eager to deepen their knowledge.
  • вопрос: What programming knowledge is required to understand the book?

    отвечать: To grasp the concepts presented in this book, a solid understanding of Python programming is essential. Readers should be familiar with basic data structures, functions, and libraries such as NumPy and Pandas. Additionally, having prior experience with machine learning concepts will enable a more effective learning process, allowing readers to appreciate the advanced deep learning models discussed.
  • вопрос: Are there any specific libraries or frameworks used in the book?

    отвечать: Yes, 'Advanced Deep Learning with Python' focuses on popular deep learning frameworks like TensorFlow and Keras. The book provides practical coding examples and detailed explanations on how to utilize these libraries effectively. This allows readers to not only learn theoretical concepts but also apply them in practical scenarios, making it a valuable resource for implementing cutting-edge deep learning models.
  • вопрос: What kind of projects or examples are included in the book?

    отвечать: The book features a variety of projects that illustrate real-world applications of deep learning, including image classification, text generation, and sentiment analysis. Each example is designed to guide readers through the implementation process, helping them understand how to design, train, and evaluate deep neural networks. This hands-on approach reinforces learning and equips readers with practical skills applicable in their own projects.
  • вопрос: Is there a focus on theory or practical applications?

    отвечать: The book strikes a balance between theoretical understanding and practical application. Each chapter typically begins with a discussion of the underlying theory, followed by practical coding examples that showcase how to implement learned techniques. This methodology ensures that readers not only grasp concepts but can also apply them directly to solve real-world problems in various fields.
  • вопрос: How does this book compare to other deep learning resources?

    отвечать: Compared to other deep learning resources, 'Advanced Deep Learning with Python' stands out for its in-depth coverage of advanced topics and real-world applications. While basic books may suffice for introductory concepts, this book provides the technical depth needed for practitioners wanting to build sophisticated models, making it highly suitable for professionals aiming to elevate their expertise in the field.
  • вопрос: Can beginners benefit from this book?

    отвечать: While the book is tailored for those with some prior knowledge, beginners can still benefit from it, especially if they are determined to learn. It's recommended that novices first familiarize themselves with basic machine learning and Python programming before diving into the advanced content. A solid foundation will enhance understanding and enable them to follow along with the advanced examples provided.
  • вопрос: Are there exercises or challenges included in the book?

    отвечать: Yes, the book includes exercises at the end of each chapter to reinforce the concepts learned. These exercises range from fundamental coding challenges to more complex problem-solving tasks that encourage creativity in applying deep learning techniques. Engaging with these challenges can significantly enhance a reader's practical skills and confidence in using deep learning effectively.
  • вопрос: Where can I buy 'Advanced Deep Learning with Python'?

    отвечать: You can buy 'Advanced Deep Learning with Python' from Ubuy, a reliable online platform that offers a wide range of books and educational resources. Ubuy provides a user-friendly shopping experience, making it easier for you to explore various options and find the book you need in Kazakhstan.

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Особенности и преимущества

  • Build faster and more robust deep learning architectures
  • Train CNN models with GPU-accelerated libraries like TensorFlow and PyTorch
  • Apply DNNs to computer vision problems, NLP, and GANs
  • Understand advanced and state-of-the-art neural network architectures
  • Learn DL techniques like meta-learning and graph neural networks
  • Ideal for data scientists, deep learning engineers, AI developers, and anyone looking to explore advanced deep learning use cases

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