Deep Learning (The MIT Press Essential Knowledge series)
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Deep Learning (The MIT Press Essential Knowledge series)
Deep learning is an artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. It enables data-driven decisions by extracting patterns from large datasets. This book offers an accessible and concise but comprehensive introduction to the fundamental technology for AI.
Deep Learning (The MIT Press Essential Knowledge series)
Numéro d'article: 21292389

Deep Learning (The MIT Press Essential Knowledge series)

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Numéro d'article: 21292389

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Deep learning is an artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. It enables data-driven decisions by extracting patterns from large datasets. This book offers an accessible and concise but comprehensive introduction to the fundamental technology for AI.
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Ce qui se démarque

Comprehensive Overview
Offers an accessible yet thorough exploration of deep learning concepts, making it perfect for both beginners and seasoned professionals seeking to refresh their knowledge.
Illustrated Insights
Richly illustrated with diagrams and visuals that clarify complex ideas, enabling readers to grasp intricate techniques and architectures effortlessly.
Authoritative Resource
Authored by leading experts in the field, this book serves as a reliable guide for students and practitioners aiming to excel in machine learning and AI applications.

Détails du produit

Shop Deep Learning (The MIT Press Essential Knowledge series) online at a best price in Mauritius. 0262537559
  • An illustrated edition of an accessible introduction to deep learning, an artificial intelligence technology
  • Explains how deep learning enables computer vision, speech recognition, machine translation, and driverless cars
  • Offers a concise but comprehensive overview of deep learning technology by computer scientist John Kelleher
  • Discusses the ability of deep learning to make data-driven decisions and its suitability for big data and computational power
  • Covers important deep learning architectures, recent developments, and fundamental algorithms like gradient descent and backpropagation
  • Considers the future of deep learning, including major trends, possible developments, and significant challenges
Publisher The MIT Press
Publication date September 10, 2019
Edition Illustrated
Language English
Print length 296 pages
ISBN-10 0262537559
ISBN-13 978-0262537551
Item Weight 8 ounces (226.8 grams)
Dimensions 5.08 x 0.75 x 7.01 inches (12.9 x 1.9 x 17.8 cm)
Part of series MIT Press Essential Knowledge

À qui est-ce destiné ?

Suitable For
  • Students of AI

    Ideal for undergraduate students seeking foundational knowledge in deep learning concepts and techniques.

  • Beginner Practitioners

    A great resource for beginners wanting to understand and implement deep learning in practical scenarios.

  • Educators and Trainers

    Useful for educators teaching deep learning as it provides clear illustrations and structured content.

Not Suitable For
  • Advanced Researchers

    Not suitable for experienced researchers looking for in-depth theoretical analysis and complex models.

DESCRIPTION DU PRODUIT

Deep Learning (The MIT Press Essential Knowledge series)

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Questions et réponses des clients

  • question: What is 'Deep Learning: The MIT Press Essential Knowledge Series Illustrated Edition' about?

    répondre: This book provides a comprehensive introduction to deep learning, a branch of artificial intelligence that focuses on algorithms inspired by the structure and function of the brain. It covers fundamental concepts, various architectures, and applications, making it an essential resource for both beginners and advanced practitioners. The illustrated edition enhances understanding with visuals, making complex topics more accessible. This book is great for students, educators, and professionals looking to grasp the principles of deep learning quickly.
  • question: Who is the intended audience for this illustrated edition?

    répondre: The illustrated edition of 'Deep Learning' is aimed at a wide range of readers, including students in computer science or data science fields, professionals looking to upskill, and instructors seeking a visual aid to teach deep learning concepts. It is particularly beneficial for those who prefer learning through illustrations, as it simplifies complex ideas and makes the learning process more engaging. This edition is also useful for self-learners who might appreciate a more visually-oriented resource.
  • question: What are the key features of the illustrated edition?

    répondre: The key features of the illustrated edition include in-depth explanations of core deep learning concepts, visually engaging illustrations that complement the text, and real-world examples that illustrate applications across various industries. Additionally, it includes exercises and tutorials designed to reinforce learning, making it both educational and practical. This edition serves as a standalone resource for beginners, while also being a reference for experienced practitioners who can benefit from visual learning.
  • question: How does this book differ from other deep learning resources?

    répondre: Unlike many traditional textbooks on deep learning that rely heavily on text, this illustrated edition emphasizes visuals to convey complex ideas effectively. It integrates diagrams and illustrations, which can aid in the reader's understanding by providing a clear visualization of concepts such as neural networks and algorithms. This unique approach enhances learning and retention, making it ideal for visual learners or those new to the field who may find standard texts daunting.
  • question: Does this book include practical examples and case studies?

    répondre: Yes, the illustrated edition includes practical examples and real-world case studies to demonstrate how deep learning concepts are applied in various fields, such as healthcare, finance, and robotics. These case studies not only contextualize the theoretical frameworks but also illustrate the impact of deep learning in solving real problems. This makes the book a valuable resource for both academic learning and professional application, allowing readers to relate their knowledge to practical scenarios.
  • question: Can this book help me with implementing deep learning models?

    répondre: Absolutely! This book not only explains theoretical concepts but also provides insights into implementing deep learning models using popular frameworks like TensorFlow and PyTorch. It includes practical tips and best practices, enabling readers to transition from theory to practice effectively. Whether you're a student working on a project or a professional looking to apply deep learning to your business, this book serves as a solid guide for implementation.
  • question: Is this book suitable for complete beginners?

    répondre: Yes, the illustrated edition is designed to be accessible for complete beginners. It starts with foundational concepts and progressively builds on them, ensuring that readers can follow along even without prior knowledge of artificial intelligence or machine learning. The use of visuals further aids comprehension, making it easier for newcomers to grasp challenging material. By the end of the book, beginners will have a solid understanding of deep learning principles and their applications.
  • question: Are there additional resources or online content that complement this book?

    répondre: Yes, many readers find that pairing the book with online resources like MOOCs, training videos, or forums dedicated to deep learning can enhance their learning experience. Websites such as Coursera, edX, and even MIT’s own course offerings often feature complementary materials. Joining online communities or discussion groups can also provide support and a platform for asking questions. This combined approach helps reinforce concepts learned from the book and fosters a deeper understanding of practical applications.
  • question: Is there a digital version available for this illustrated edition?

    répondre: Yes, the illustrated edition of 'Deep Learning' is typically available in digital formats such as eBook or PDF. This digital version allows for convenient reading on various devices, making it easier for readers to access content on-the-go. Additionally, digital formats often offer features like search functions, annotations, and adjustable text sizes, enhancing the overall reading experience. It's ideal for students and professionals who prefer a flexible and portable learning format.
  • question: Where can I buy 'Deep Learning: The MIT Press Essential Knowledge Series Illustrated Edition' in Mauritius?

    répondre: You can purchase 'Deep Learning: The MIT Press Essential Knowledge Series Illustrated Edition' from Ubuy in Mauritius. Ubuy is a reliable e-commerce platform that offers a wide range of books, including this illustrated edition. With Ubuy, you can enjoy a seamless shopping experience, along with options for fast shipping and customer support to assist you with your purchase.

Intelligence & Semantics Editorial Review

Deep Learning is a concise and informative book that provides a layman's introduction to the fundamental concepts of deep learning. Unlike other books, this text is not overly mathematical and is an excellent resource for beginners. The book starts with the basics and builds upon them systematically. The explanations are clear and straightforward, and the content is well-written. The book is also an excellent refresher for people who are already knowledgeable on the subject. The pages are smaller than average, but this doesn't take away from the value of the content. Overall, Deep Learning is an excellent introduction to the field of deep learning, and it provides an excellent foundation for students and professionals alike.

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Avantages

  • Clear and concise explanations
  • Beginner-friendly introduction to deep learning
  • Well-written content
  • Good refresher for people already knowledgeable about the subject
  • Provides an excellent foundation for students and professionals alike

Les inconvénients

  • Half the size of the typical book, which may affect the reading experience

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Caractéristiques et avantages

  • An accessible introduction to deep learning Technology
  • Explains deep learning enables data-driven decisions by extracting patterns from big data
  • Covers deep learning architectures including Generative Adversarial Networks and capsule networks
  • Provides comprehensive introduction to fundamental algorithms in deep learning
  • Considers the future of deep learning and major trends, possible developments, and significant challenges.

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