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26537460
Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and
88% of respondents would recommend this to a friend
Item #:
26537460
XOF 38340
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What Stands Out
Comprehensive Coverage
This book provides extensive insights into advanced deep learning techniques, covering GANs, VAEs, and more, catering to both beginners and seasoned professionals eager to enhance their skills.
Hands-on Approach
With practical examples and exercises using TensorFlow 2 and Keras, readers gain hands-on experience, ensuring a deeper understanding of complex algorithms and real-world applications.
Updated Content
The second edition includes the latest advancements and best practices in deep learning, ensuring that readers are equipped with current knowledge in a rapidly evolving field.
Product Details
| Publisher | Packt Publishing |
| Publication date | February 28, 2020 |
| Edition | 2nd ed. |
| Language | English |
| Print length | 512 pages |
| ISBN-10 | 1838821651 |
| ISBN-13 | 978-1838821654 |
| Item Weight | 2.02 pounds (920 grams) |
| Dimensions | 7.5 x 1.16 x 9.25 inches (19.1 x 2.9 x 23.5 cm) |
Who Should Buy?
-
Deep Learning Practitioners
Ideal for those wanting to deepen their understanding of deep learning techniques using TensorFlow 2 and Keras.
-
AI Students
Students pursuing AI or machine learning can benefit from practical examples and comprehensive content on advanced topics.
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Professionals Transitioning
Professionals looking to expand their skill set in deep learning applications and frameworks will find this valuable.
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Beginners
Complete beginners in machine learning may struggle without foundational knowledge prior to this advanced text.
Product Description
Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition
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Intelligence & Semantics Editorial Review
In "Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition," the book is praised for being an excellent resource for individuals interested in learning about deep learning on MNIST applications using different approaches. Algorithms are well detailed and illustrated, making it easy to understand. The book covers the core ML models, MLP, CNN, and RNN, and advanced techniques for each. However, there is feedback that the book is primarily aimed at computer vision techniques, with some reinforcement learning towards the end, and is not helpful for users interested in natural language processing techniques. Explanations are limited, and it is suggested that users purchase a companion book for more in-depth knowledge. The book is well written and suitable for readers who already have a basic understanding of Deep Learning concepts and wish to expand their knowledge.
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Pros
- Well-detailed and illustrated algorithms
- Excellent resource for learning about deep learning on MNIST applications using different approaches
- Covers the core ML models, MLP, CNN, and RNN, and advanced techniques for each
- Suitable for readers who already have a basic understanding of Deep Learning concepts and wish to expand their knowledge
Cons
- Primarily aimed at computer vision techniques, with limited reinforcement learning
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