Deep Learning in Modern C++: End-to-end development and implementation of deep learning algorithms (English Edition)
Deep Learning in Modern C++: End-to-end development and implementation of deep learning algorithms (English Edition)
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Deep Learning in Modern C++: End-to-end development and implementation of deep learning algorithms (English Edition)
Deep Learning in Modern C++: End-to-end development and implementation of deep learning algorithms (English Edition)
This book empowers developers to build sophisticated AI applications from the ground up using modern C++ and deep learning.
Deep Learning in Modern C++: End-to-end development and implementation of deep learning algorithms (English Edition)
Numéro d'article: 142738956

Deep Learning in Modern C++: End-to-end development and implementation of deep learning algorithms (English Edition)

Numéro d'article: 142738956

XAF 25968

XAF 34931

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Ce qui se démarque

Comprehensive Guide
Covers deep learning concepts from basics to advanced topics in an accessible way, making it suitable for both beginners and experienced C++ developers seeking to enhance their skills.
Practical Implementation
Offers step-by-step coding examples that allow readers to implement algorithms hands-on, empowering them to transition theoretical knowledge into practical skills efficiently.
Focused on C++
Specifically tailored for C++ programming, this book leverages the language's strengths, providing insights into performance optimization and system-level programming for deep learning applications.

Détails du produit

Shop Deep Learning in Modern C++: End-to-end development and implementation of deep learning algorithms (English Edition) online at a best price in Cameroon. 9365893518
  • DescriptionDeep learning is revolutionizing how we approach complex problems, and harnessing its power directly within C++ provides unparalleled control and efficiency. This book bridges the gap between cutting-edge deep learning techniques and the robust, high-performance capabilities of modern C++, empowering developers to build sophisticated AI applications from the ground up.This book guides you through the entire development lifecycle, starting with a solid foundation in the modern features and essential libraries, like Eigen, for C++. You will master core deep learning concepts by implementing convolutions, fully connected layers, and activation functions, while learning to optimize models using gradient descent, backpropagation, and advanced optimizers like SGD, Momentum, RMSProp, and Adam. Crucial topics like cross-validation, regularization, and performance evaluation are covered, ensuring robust and reliable applications. Finally, you will dive into computer vision, building image classifiers and object localization systems, leveraging transfer learning for optimal performance.By the end of this book, you will be proficient in developing and deploying deep learning models within C++, equipped with the tools and knowledge to tackle real-world AI challenges with confidence and precision.What you will learn● Implement core deep learning models in modern C++.● Code CNNs, RNNs, GANs, and optimization techniques.● Build and test robust deep learning C++ applications.● Apply transfer learning in C++ computer vision tasks.● Master backpropagation and gradient descent in C++.● Develop image classifiers and object detectors in C++.Who this book is forThis book is tailored for C++ developers, data scientists, and machine learning engineers seeking to implement deep learning models using modern C++. A foundational understanding of C++ programming and basic linear algebra is recommended.Table of Contents1. Introduction to Deep Learning Programming2. Coding Deep Learning with Modern C++3. Testing Deep Learning Code4. Implementing Convolutions5. Coding the Fully Connected Layer6. Learning by Minimizing Cost Functions7. Defining Activation Functions8. Using Pooling Layers9. Coding the Gradient Descent Algorithm10. Coding the Backpropagation Algorithm11. Underfitting, Overfitting, and Regularization12. Implementing Cross-validation, Mini Batching, and Model Performance Metrics13. Implementing Optimizers14. Introducing Computer Vision Models15. Developing an Image Classifier16. Leveraging Training Performance with Transfer Learning17. Developing an Object Localization System
Publisher BPB Publications
Publication date April 23, 2025
Language English
Print length 462 pages
ISBN-10 9365893518
ISBN-13 978-9365893519
Item Weight 1.74 pounds (790 grams)
Dimensions 7.5 x 1.05 x 9.25 inches (19.1 x 2.7 x 23.5 cm)

À qui est-ce destiné ?

Suitable For
  • C++ Developers

    Ideal for developers familiar with C++ wanting to implement deep learning algorithms effectively in their applications.

  • Data Scientists

    Data scientists looking to integrate deep learning techniques into their workflows will find practical insights and implementations.

  • Machine Learning Enthusiasts

    Enthusiasts curious about deep learning concepts and C++ applications will appreciate the detailed explanations and examples.

Not Suitable For
  • Beginner Programmers

    Not suitable for beginners who lack foundational programming skills or experience using C++, especially in complex topics.

  • Non-Technical Users

    Individuals without a technical background may struggle to grasp the advanced concepts and coding examples presented.

  • Python Users

    Users accustomed to Python for deep learning might find transitioning to C++ challenging and counterintuitive for their projects.

DESCRIPTION DU PRODUIT

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Questions et réponses des clients

  • question: What topics does 'Deep Learning in Modern C++' cover?

    répondre: The book covers a comprehensive range of topics including neural network architecture, activation functions, optimization algorithms, and practical implementation of deep learning algorithms using C++. It emphasizes integrating deep learning concepts with C++ applications, making it suitable for developers who wish to leverage the power of C++ in AI development. Readers will also find examples and projects that enhance their understanding of both deep learning theory and practical coding.
  • question: Who is the target audience for this book?

    répondre: The primary audience includes programmers and developers with a background in C++ who want to delve into deep learning. This book is particularly beneficial for those familiar with machine learning concepts, as it bridges the gap between theoretical knowledge and practical coding. Additionally, data scientists and researchers looking to implement deep learning models in C++ will find valuable insights and techniques to enhance their projects.
  • question: Can beginners understand this book, or is it meant for advanced readers?

    répondre: While the book assumes a basic understanding of programming in C++, it is designed to guide readers through complex concepts gradually. Beginners can grasp the material if they have foundational knowledge of programming and some exposure to machine learning principles. The book includes clear explanations and practical examples that help simplify the learning process, making it accessible for anyone eager to explore deep learning in C++.
  • question: What programming skills will I improve by reading this book?

    répondre: Readers will significantly enhance their C++ programming skills, particularly in relation to implementing algorithms commonly used in deep learning. The book offers hands-on coding examples that focus on optimizing performance and efficiency, which are critical in AI development. By applying what they've learned, readers can confidently tackle real-world machine learning projects and contribute to developments in this rapidly evolving field.
  • question: Does the book provide practical examples and projects?

    répondre: Yes, the book is rich with practical examples and projects that illustrate how to apply deep learning algorithms using C++. These hands-on projects guide readers through real-world applications, reinforcing theoretical knowledge with practical experience. This approach not only aids in understanding the material but also equips readers with the skills to implement their own deep learning solutions in various domains like image processing, natural language processing, and more.
  • question: What are some key concepts I should know before starting the book?

    répondre: Before diving into the book, it’s helpful to have a foundational understanding of linear algebra, calculus, and basic probability. Familiarity with the structure of neural networks and essential machine learning principles will also enhance your comprehension of the material. These concepts will provide a solid framework for grasping the more complex topics discussed in the book, allowing you to implement algorithms effectively.
  • question: How does C++ benefit deep learning implementation?

    répondre: C++ offers performance advantages crucial for deep learning applications, such as high-speed computation and efficient memory management. Its ability to interact with low-level system components also enables developers to optimize algorithms for enhanced performance. For developers aiming to create production-grade applications, C++ provides the robustness and efficiency needed to support large-scale neural networks and real-time data processing.
  • question: Is this book suitable for implementing deep learning in production environments?

    répondre: Absolutely. 'Deep Learning in Modern C++' not only covers theoretical aspects of deep learning but also emphasizes practical implementation strategies suitable for production environments. The book discusses best practices for developing scalable and efficient models, making it a valuable resource for those looking to deploy deep learning applications in real-world scenarios across various industries.
  • question: Are there additional resources recommended for deeper learning?

    répondre: In addition to the book, readers may benefit from online courses, forums, and communities dedicated to deep learning and C++. Platforms like Coursera and edX offer courses that complement the materials covered in the book. Engaging with communities on platforms like GitHub or Stack Overflow can also provide practical insights and encourage collaborative learning.
  • question: Where can I buy 'Deep Learning in Modern C++: End-to-end development and implementation of deep learning algorithms (English Edition)'?

    répondre: You can purchase 'Deep Learning in Modern C++: End-to-end development and implementation of deep learning algorithms (English Edition)' on Ubuy. Ubuy offers a convenient buying experience, with options that cater to customers in Cameroon. Simply navigate to their website, search for the book title, and follow the prompts to complete your purchase.

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Avantages

  • Comprehensive coverage of deep learning
  • Hands-on code examples
  • Clear and understandable explanations
  • Up-to-date with current trends
  • Great for both beginners and experts

Les inconvénients

  • Some topics might be overwhelming for total beginners.

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XAF 25968

XAF 34931

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Ubuy s'engage à protéger votre sécurité et votre confidentialité. Notre système avancé de sécurité des paiements garantit la confidentialité en chiffrant vos informations lors de la transmission grâce aux protocoles AES (Advanced Encryption Standards) et SSL (Secure Socket Layer). Vos coordonnées de paiement sont 100 % sécurisées car nous ne partageons pas vos informations de paiement avec des vendeurs tiers.

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Caractéristiques et avantages

  • Learn to implement core deep learning models in modern C++.
  • Master convolutional networks (CNNs), recurrent networks (RNNs), and GANs.
  • Build and test robust deep learning applications tailored for C++.
  • Apply advanced optimization techniques like gradient descent and backpropagation.
  • Develop image classifiers and object detection systems using transfer learning.
  • Gain confidence in tackling real-world AI challenges with precision.