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منتج #:
117993479
Deep Learning (Adaptive Computation and Machine Learning series)
88% من المشترين سيوصون بهذا المنتج لصديق
منتج #:
117993479
JOD 58
تفاصيل السعر
باستثناء رسوم الشحن والجمارك ( سيتم احتساب رسوم الشحن والجمارك عند إتمام الشراء )
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“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”—Elon Musk
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Comprehensive Coverage
This book provides an exhaustive overview of deep learning, covering essential algorithms and architectures that empower readers to grasp complex concepts and practical applications effectively.
Adaptive Learning
Utilizing the latest adaptive computation techniques, this edition ensures readers learn at their own pace, making it suitable for both beginners and advanced practitioners in the field.
Practical Examples
Rich with real-world examples and case studies, this resource illustrates theoretical concepts in a hands-on manner, equipping readers with the skills to implement deep learning in various domains.
تفاصيل المنتج
- An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
| Publisher | The MIT Press |
| Publication date | November 10, 2016 |
| Language | English |
| File size | 17.3 MB |
| Screen Reader | Supported |
| Enhanced typesetting | Enabled |
| X-Ray | Not Enabled |
| Word Wise | Not Enabled |
| Print length | 800 pages |
| ISBN-13 | 978-0262337373 |
| Page Flip | Enabled |
| Item Weight | 0.5 lbs (230 grams) |
من يجب أن يشتري؟
-
Aspiring Data Scientists
Individuals seeking foundational knowledge in deep learning to kickstart their careers in data science and analytics.
-
Academic Researchers
Researchers looking for a comprehensive guide to deep learning concepts for their academic projects and publications.
-
Software Developers
Developers who want to implement deep learning techniques in applications and improve their machine learning skills.
-
Beginner Learners
Users with no prior knowledge of machine learning may find the content too complex and challenging to follow.
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أسئلة العملاء & الإجابات
-
سؤال:
Who are the authors of Deep Learning?
إجابه: The book is written by three experts in the field. -
سؤال:
What topics does the book cover?
إجابه: It covers mathematical foundations, deep learning techniques, and various applications. -
سؤال:
Is this book suitable for beginners?
إجابه: Yes, it is suitable for undergraduate and graduate students, as well as software engineers.
Intelligence & Semantics Editorial Review
**** The "Deep Learning" book from the Adaptive Computation and Machine Learning series exhibits an adept balance between foundational concepts and cutting-edge research, positioning itself as an essential resource for those serious about the deep learning field. Reviewers commend its extensive coverage of both basic and advanced topics, encapsulating essential elements like linear algebra, optimization, performance metrics, and practical aspects, along with different network architectures such as multi-layer perceptrons and recurrent neural networks. What stands out is the substantial "Deep Learning Research" section that spans over 235 pages, highlighting contemporary research curated by seasoned authors. This selection and cohesive discussion of advanced concepts is highly appreciated, suggesting that the book serves not only as a knowledge resource but also as an integrative summary of current trends in the field. The book is deemed particularly beneficial for readers with a solid grounding in mathematics and machine learning principles. It serves as an advanced follow-up for those who have a foundational understanding of concepts and are looking to explore deeper complexities in neural networks. The insightful observations and mathematical derivations throughout are well-received, enabling an intuitive grasp of advanced ideas and their applications. However, the book is not designed as a programming guide, leading to some misunderstandings among readers expecting it to teach practical coding skills in machine learning systems. Those unfamiliar with the fundamentals of deep learning may find it challenging and perhaps less rewarding. The emphasis on advanced topics means that this book ideally caters to an audience already on the path of specialized study, enhancing their understanding and pushing their capabilities further. Overall, the "Deep Learning" book is well-regarded as an invaluable asset to learners and researchers alike, promising to elevate the reader's comprehension and engagement within the evolving landscape of deep learning. **
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إيجابيات
- Comprehensive coverage of deep learning fundamentals and advanced topics.
- In-depth analysis with mathematical derivations and intuitive illustrations.
- Valuable insights from experienced authors on current research trends.
- Suitable for individuals with a solid background in linear algebra, calculus, and machine learning.
- Acts as a robust follow-up resource for readers familiar with basic neural network concepts.
سلبيات
- Not intended as a coding or practical implementation guide, which may mislead some readers.
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JOD 58
اطلب الآن واحصل عليه حول الجمعة, أكتوبر 09
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المميزات والفوائد
- Comprehensive introduction to deep learning concepts and techniques.
- Covers essential mathematical and conceptual foundations.
- Explains industry-used deep learning methods and applications.
- Ideal for students and software engineers entering the field.
- Includes supplementary material for enhanced learning.
- Recommended by industry leaders like Elon Musk.
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