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Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition
Build powerful, robust, and accurate predictive models with the power of TensorFlow.
Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition
Item #: 59849065

Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition

Item #: 59849065

€ 46

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What Stands Out

Comprehensive Coverage
This edition provides an in-depth exploration of neural networks, equipping readers with the necessary foundations to understand and implement advanced deep learning systems using TensorFlow and Python.
Hands-On Projects
Offers practical projects and examples that guide users through real-world applications, ensuring they can apply theoretical knowledge while gaining hands-on experience with TensorFlow.
Updated Techniques
Includes the latest advancements in deep learning technologies and TensorFlow updates, ensuring readers are learning the most current practices in this rapidly evolving field.

Product Details

Shop Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition online at a best price in Latvia. 1788831101
Publisher Packt Publishing
Publication date March 30, 2018
Edition 2nd Revised edition
Language English
Print length 276 pages
ISBN-10 1788831101
ISBN-13 978-1788831109
Item Weight 1.82 pounds (830 grams)
Dimensions 7.5 x 1.09 x 9.25 inches (19.1 x 2.8 x 23.5 cm)

Who Should Buy?

Suitable For
  • Beginner Learners

    Ideal for individuals new to deep learning, offering foundational concepts and practical examples for easy understanding.

  • AI Practitioners

    Suitable for professionals looking to enhance their skills in building neural networks using TensorFlow and Python.

  • Academics & Researchers

    Useful for students and researchers wanting to explore advanced deep learning techniques in a structured manner.

Not Suitable For
  • Advanced Experts

    Not suitable for experienced practitioners seeking in-depth theoretical frameworks or cutting-edge research methodologies.

Product Description

Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition

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Software Development Editorial Review

**** "Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition" presents a mixed experience for readers diving into the world of deep learning. The book boasts a well-organized overview of TensorFlow, which many users appreciated as a solid introduction to the subject. It stands out by integrating the native TensorFlow framework rather than solely relying on Keras, offering a deeper insight into the functionality of the library. However, these strengths are somewhat overshadowed by critical feedback regarding the amount of explanation accompanying the code. While the authors do provide examples and demonstrate coding practices, many readers felt that the explanations lacked sufficient depth and clarity. Users pointed out that the book often reads like a code repository rather than a comprehensive instructional guide, making it challenging for less experienced readers or those new to neural networks to fully grasp the concepts presented. The haste in which the code was explained left several feeling that they needed more thorough guidance, especially regarding the rationale behind specific choices in the coding process. Despite its shortcomings, the book is still viewed as an effective resource for those who are already familiar with foundational concepts in neural networks. More advanced learners can benefit from the structured approach to learning TensorFlow, while beginners may find themselves navigating a steep learning curve without adequate support. **

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Pros

  • Comprehensive overview of TensorFlow.
  • Utilizes native TensorFlow, providing a deeper understanding.
  • Structured approach to learning deep learning concepts.

Cons

  • Explanations accompanying code are often sparse and lacking detail.

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