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Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition
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What Stands Out
Product Details
| 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?
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Beginner Learners
Ideal for individuals new to deep learning, offering foundational concepts and practical examples for easy understanding.
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AI Practitioners
Suitable for professionals looking to enhance their skills in building neural networks using TensorFlow and Python.
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Academics & Researchers
Useful for students and researchers wanting to explore advanced deep learning techniques in a structured manner.
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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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