What topics are covered in Grokking Deep Learning First Edition?

Grokking Deep Learning First Edition delves into essential subjects such as neural networks, backpropagation, and deep learning frameworks. It employs intuition-driven explanations and practical examples, making complex theories accessible. The book is ideal for beginners seeking to understand deep learning concepts as it illustrates how to build, train, and deploy neural networks using hands-on coding exercises.

Who is the author of Grokking Deep Learning First Edition?

Grokking Deep Learning First Edition is authored by Andrew Trask, a recognized expert in artificial intelligence and deep learning. Trask’s approachable writing style simplifies sophisticated topics and provides clarity for those new to the field. His experience in teaching and research in deep learning underpins the book's innovative and engaging pedagogical approach, making it a valuable resource.

Is Grokking Deep Learning First Edition suitable for beginners?

Yes, Grokking Deep Learning First Edition is particularly designed for individuals new to deep learning. It breaks down complex concepts into digestible parts, using intuitive analogies and real-world examples. Whether you are a student, professional, or hobbyist, the book equips you with foundational knowledge, guiding you through coding exercises to help reinforce your learning effectively.

What coding languages are used in Grokking Deep Learning First Edition?

The book primarily uses Python and the TensorFlow library to showcase deep learning techniques. Python’s simplicity and popularity in the data science community make it an ideal choice for learners. By working with TensorFlow, readers can build and train models while grasping the underlying principles, providing practical experience that is directly applicable to real-world projects.

Does Grokking Deep Learning First Edition include practical exercises?

Absolutely, Grokking Deep Learning First Edition is packed with practical coding exercises designed to solidify understanding of deep learning concepts. Each chapter features hands-on projects that allow readers to apply what they've learned in real scenarios, which enhances their coding skills. This practical approach prepares readers to tackle actual deep learning problems they may encounter in their careers or studies.

Can I apply concepts learned from Grokking Deep Learning First Edition to real-world projects?

Yes, the concepts from Grokking Deep Learning First Edition are directly applicable to real-world projects. The knowledge gained can be used in fields such as image recognition, natural language processing, and more. By working through the practical exercises, you'll be well-equipped to implement deep learning solutions in various industries, making it a useful resource for aspiring professionals in tech.

Are there any prerequisites for reading Grokking Deep Learning First Edition?

While no extensive background in machine learning is required, a basic understanding of programming (preferably Python) and some familiarity with mathematical concepts like calculus and linear algebra can enhance your learning experience. The book gradually introduces more advanced topics, so even if you are relatively new to these concepts, you can still progress effectively through the material.

How does Grokking Deep Learning First Edition differ from other deep learning books?

Grokking Deep Learning First Edition distinguishes itself with its focus on intuition and clarity. While many deep learning texts can be dense and theoretical, this book prioritizes a visual and interactive approach. It aims to cultivate a solid conceptual understanding, allowing learners to grasp the 'why' behind techniques, making it particularly engaging for those new to the field.

Is there a digital version of Grokking Deep Learning First Edition available?

Yes, Grokking Deep Learning First Edition is available in both print and digital formats. The digital version allows for convenient reading on various devices, making it accessible anytime, anywhere. This flexibility makes it easier for learners to engage with the material, especially when on the go or while working on projects, thus enhancing the overall learning experience.

Where can I buy Grokking Deep Learning First Edition in Taiwan?

You can buy Grokking Deep Learning First Edition in Taiwan through Ubuy. This platform offers a variety of options for purchasing books and ensures a streamlined shopping experience with various payment methods. Ubuy provides reliable access to a wide range of products, including popular tech and coding resources like this book, making it easy to find and acquire.

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Grokking Deep Learning teaches you to build deep learning neural networks from scratch!

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Grokking Deep Learning

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What Stands Out

User-Friendly Approach

Grokking Deep Learning simplifies complex concepts through intuitive explanations and visual aids, making deep learning accessible to beginners and non-experts.

Hands-On Learning

The book emphasizes practical applications and includes coding exercises that empower readers to implement deep learning techniques effectively and confidently.

Comprehensive Coverage

It offers an extensive overview of deep learning topics, ensuring readers gain a solid foundation and understanding of advanced techniques and their real-world applications.

產品詳情

Introduces Artificial Intelligence and Deep Learning as revolutionary technologies
Explains the behavior of neural networks based on human brains
Suitable for readers with high school-level math and intermediate programming skills
Covers building neural networks for image recognition, language translation, and gaming
Provides insights into the impact of Deep Learning on various industries
Includes guidance on understanding algorithms and the principles of Deep Learning

Publisher	Manning
Publication date	January 25, 2019
Edition	First Edition
Language	English
Print length	336 pages
ISBN-10	1617293709
ISBN-13	978-1617293702
Item Weight	1.26 pounds (570 grams)
Dimensions	7.38 x 0.7 x 9.25 inches (18.7 x 1.8 x 23.5 cm)

Who Should Buy?

Beginners in AI
Ideal for individuals new to artificial intelligence concepts and looking for an accessible introduction to deep learning.
Self-Learners
Great for those pursuing independent study and seek practical examples to understand deep learning applications.
Educators
Useful resource for teachers wanting to introduce deep learning concepts in an engaging and comprehensible manner.

Advanced Practitioners
Not suitable for experienced professionals who require in-depth content or advanced technical discussions on deep learning.

產品描述

Grokking Deep Learning

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問題: 如何從 Ubuy 在線購物 Grokking Deep Learning？

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問題: Grokking Deep Learning 可以在 Taiwan 在線購物嗎？

Answer: 是的，您可以在 Ubuy Taiwan 以合理的價格購買該產品。. Grokking Deep Learning 在本地不可用，但您可以信任我們的快遞服務。
問題: 下訂單後需要多長時間才能收到產品？

Answer: 您訂購的產品的交貨時間根據您訂購的商品和您選擇的運輸方式而有所不同。. 結賬時會提到預計送貨時間，所以購物時請放心。

Neural Networks Editorial Review

Andrew W. Trask's "Grokking Deep Learning" is a great introduction to deep learning for people who want to learn the basics. His writing is clear and easy to understand, and he covers several known issues with deep learning while providing many useful examples. While not the best book for people with a Ph.D. in mathematics, it's a perfect starting point for beginners who want to get hands-on experience with implementing neural networks. However, the book has several syntax and coding issues that are easy to detect by a trained eye but not as easy for a novice learner. Overall, the methodology for developing the deep networks and understanding them are there, but the examples chosen can be ridiculous.

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優點

Easy to read
Hands-on approach to implementing neural networks
Provides guidance on next steps
Clear explanations

缺點

Typographical errors throughout the book

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特色和優勢

Learn to build deep learning neural networks from scratch
Teaches you the science behind neural network training
Only uses Python and NumPy library
Suitable for readers with high school-level math and intermediate programming skills
Includes a free eBook with the purchase of print book
Introduces readers to federated learning

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Neural Networks Editorial Review

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TWD 1883

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