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- Deep Learning with R, Second Edition
Deep Learning with R, Second Edition
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EGP 3951
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Deep Learning with R, Second Edition shows you how to put deep learning into action. It’s based on the revised new edition of François Chollet’s bestselling Deep Learning with Python.
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تفاصيل المنتج
| Publisher | Manning |
| Publication date | July 26, 2022 |
| Edition | 2nd |
| Language | English |
| Print length | 568 pages |
| ISBN-10 | 1633439844 |
| ISBN-13 | 978-1633439849 |
| Item Weight | 1.9 pounds (860 grams) |
| Dimensions | 7.38 x 1.3 x 9.25 inches (18.7 x 3.3 x 23.5 cm) |
من يجب أن يشتري؟
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Beginners in Data Science
Newcomers can benefit from clear explanations and practical examples to start their deep learning journey effectively.
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R Enthusiasts
Users with a background in R programming will appreciate deep learning concepts tailored specifically for the R ecosystem.
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Academic Researchers
Researchers aiming to apply deep learning techniques in their projects will find comprehensive insights and methodologies.
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Advanced Machine Learning
Experienced practitioners may find the content too basic and not challenging enough for advanced deep learning topics.
وصف المنتج
Deep Learning with R, Second Edition
أسئلة العملاء & الإجابات
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سؤال:
What is 'Deep Learning with R, Second Edition' about?
إجابه: The book 'Deep Learning with R, Second Edition' provides an extensive introduction to deep learning concepts and techniques, particularly focusing on using the R programming language. This edition is particularly beneficial for data scientists and analysts looking to dive deep into neural network architectures and algorithms. The practical applications included enhance understanding by offering real-world scenarios. Users will learn how to implement deep learning techniques in R using popular packages like Keras and TensorFlow, making it an essential resource for both beginners and experienced practitioners. -
سؤال:
Who is the author of 'Deep Learning with R, Second Edition'?
إجابه: The author of 'Deep Learning with R, Second Edition' is François Chollet, a prominent figure in the deep learning community known for creating the Keras library. His expertise in artificial intelligence and machine learning allows readers to gain insights from someone with extensive experience in the field. Chollet's writing style and methodological approach make complex concepts accessible. Coupled with his work on Keras, this book reflects both theoretical understanding and practical applications essential for professionals wanting to enhance their skills in deep learning using R. -
سؤال:
What topics are covered in the book?
إجابه: The book covers a wide range of topics including the fundamentals of deep learning, constructing and training neural networks, convolutional networks, and recurrent neural networks, among others. It also touches on critical areas such as model evaluation, optimization techniques, and transfer learning. By systematically guiding the reader through the essential build-up of knowledge, this resource prepares users to tackle varying deep learning challenges. It’s ideal for those wanting both a solid theoretical foundation and practical tools to build their own deep learning models. -
سؤال:
Is this book suitable for beginners in deep learning?
إجابه: Yes, 'Deep Learning with R, Second Edition' is suitable for beginners. The book begins with fundamental concepts and gradually transitions into more complex topics, ensuring that readers build a strong foundation. It provides code examples and exercises that reinforce learning, making it easier for newcomers to grasp deep learning principles without feeling overwhelmed. Users can start with basic neural network principles and move towards developing sophisticated models, which will enhance their competencies in R programming and machine learning. -
سؤال:
What programming knowledge is required to follow this book?
إجابه: To effectively utilize 'Deep Learning with R, Second Edition,' readers should have a basic understanding of the R programming language. Familiarity with data manipulation and basic statistical concepts would also be advantageous. Although the book includes step-by-step explanations, prior programming experience will help readers navigate code examples more efficiently. This makes it a practical resource for those already engaged in data science or analytics, as they can directly apply their existing skills while learning deeper machine learning techniques. -
سؤال:
Are there exercises and practical examples in the book?
إجابه: Yes, the book includes numerous exercises and practical examples quite beneficial for solidifying knowledge. These exercises not only allow readers to apply what they have learned but also encourage them to experiment with different datasets and techniques. This hands-on approach is crucial for understanding deep learning concepts in a practical context. By engaging with real-world data, users can see how theory translates into practice, enhancing their problem-solving skills in various scenarios relating to data-driven decision-making. -
سؤال:
What are the benefits of using R for deep learning?
إجابه: Using R for deep learning has several benefits, including its rich ecosystem of packages for data analysis and visualization. R is particularly favored in academic and research circles, providing users with a wide array of tools that facilitate statistical modeling and graphical data representation. Furthermore, libraries like Keras and TensorFlow have been integrated into R, allowing users to leverage its powerful capabilities for building and training deep learning models. This enhances productivity and offers a smooth workflow for data scientists working on predictive modeling. -
سؤال:
Can this book help in preparing for machine learning certifications?
إجابه: Absolutely, 'Deep Learning with R, Second Edition' can be a valuable resource for those preparing for machine learning certifications. The comprehensive coverage of deep learning principles and practices equips readers with the knowledge needed to understand complex machine learning concepts fully. Additionally, the practical applications and hands-on exercises within the book make it easier for individuals to synthesize theoretical knowledge into practical skills. As a result, users can confidently tackle certification exam topics and demonstrate a well-rounded expertise in deep learning. -
سؤال:
What editions are available for 'Deep Learning with R'?
إجابه: 'Deep Learning with R, Second Edition' is the latest edition available, reflecting the most current developments in deep learning practices. This edition offers updated content, additional algorithms, and enhanced practical examples compared to its predecessor. As technology in the field continues to evolve, having the latest edition ensures that readers are learning the most relevant and effective methods. It's recommended for both newcomers and those familiar with previous editions, as the updates provide more depth and clarity on advanced deep learning topics. -
سؤال:
Where can I buy 'Deep Learning with R, Second Edition' in Egypt?
إجابه: You can buy 'Deep Learning with R, Second Edition' from Ubuy, a reliable online platform offering a variety of books and educational resources. Ubuy ensures a wide selection and provides easy navigation for finding specific items like this book. It's a great option for individuals in Egypt looking to quickly obtain this essential resource on deep learning using R for their professional or academic endeavors.
Intelligence & Semantics Editorial Review
Deep Learning with R, Second Edition is a comprehensive guide published by Manning on July 26, 2022. This second edition spans 568 pages and is designed for English readers eager to dive into the world of deep learning using R. Readers appreciate its clear explanations and practical examples, which help in understanding complex concepts effectively. The book has a user-friendly layout, making it easy to navigate through various topics, and its substantial length allows for in-depth exploration of modern deep learning techniques. This edition also reflects the latest advancements in the field, keeping readers updated.
مراجعات العملاء وتقييماتهم
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إيجابيات
- Comprehensive guide on deep learning concepts
- User-friendly layout for easy navigation
- Includes practical examples for better understanding
- Reflects the latest advancements in deep learning
- Ideal for both beginners and experienced users
سلبيات
- Available only in English language
منصة موثوقة وثقة كاملة للمشتري
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EGP 3951
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المميزات والفوائد
- Learn deep learning from first principles using R and Keras library
- Gain practical techniques for building neural networks
- Covers core DL tasks like image classification, time series forecasting, and text manipulation
- Adapted from François Chollet’s bestselling Deep Learning with Python
- Ideal for readers with intermediate R skills and no previous deep learning experience
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