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71532216
Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition
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Item #:
71532216
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What Stands Out
Comprehensive Guide
This edition covers supervised, unsupervised, and reinforcement learning, providing a holistic understanding for beginners and experienced practitioners alike, enhancing skills in diverse AI applications.
Hands-On Projects
Engage in practical projects that reinforce learning, ensuring users gain real-world experience in building and deploying deep learning models using TensorFlow and Keras.
Updated Content
The third edition features the latest advancements in TensorFlow and Keras, ensuring that users are equipped with contemporary knowledge and tools to stay competitive in the rapidly evolving AI field.
Product Details
| Publisher | Packt Publishing |
| Publication date | October 6, 2022 |
| Edition | 3rd ed. |
| Language | English |
| Print length | 698 pages |
| ISBN-10 | 1803232919 |
| ISBN-13 | 978-1803232911 |
| Item Weight | 2.6 pounds (1.18 kg) |
| Dimensions | 7.5 x 1.58 x 9.25 inches (19.1 x 4 x 23.5 cm) |
Who Should Buy?
-
Aspiring Data Scientists
Ideal for beginners seeking to learn deep learning fundamentals using TensorFlow and Keras effectively.
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Machine Learning Practitioners
Beneficial for professionals wanting to expand their knowledge in deep learning applications and model deployment.
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Academic Researchers
Useful for academics looking to implement and experiment with various deep learning models in research projects.
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Complete Beginners
Those with no programming or mathematical background might struggle to grasp advanced concepts quickly.
Product Description
Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition
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Expert Systems Editorial Review
The "Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition" offers a comprehensive overview of machine learning methods but is marred by numerous typos throughout the content, specifically in equations and model results. The poor print quality of the book and its sheer weight make handling it cumbersome, prompting the suggestion to divide it into multiple volumes. Furthermore, the book assumes a pre-existing familiarity with neural networks, forgoing detailed explanations of concepts and resorting to complex glossaries without sufficient background information. However, the extensive code samples and examples are lauded as invaluable resources for learning deep learning systems, particularly for individuals new to the field. While the book's thorough coverage of machine and deep learning concepts is appreciated, the lack of detail and vague explanations have been critiqued. **
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Pros
- Offers a wide array of machine learning methods
- Extensive code samples and examples
- Thorough coverage of machine and deep learning concepts
- A valuable resource for individuals new to the field
Cons
- Littered with typos, particularly in equations and model results
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