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Item #:
18293041
Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras
75% of respondents would recommend this to a friend
Item #:
18293041
BAM 135
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What Stands Out
Extensive Recipes
Offers over 100 practical recipes to help users effectively implement generative models, making complex techniques accessible and practical for both beginners and experienced practitioners in machine learning.
Comprehensive Frameworks
Utilizes popular tools like Python, TensorFlow, and Keras, ensuring compatibility and ease of use for developers, while promoting effective learning and application in real-world scenarios.
Expert Guidance
Written by knowledgeable authors, this guide provides expert insights and clear explanations, empowering readers to build robust generative models and enhancing their skills in deep learning technologies.
Product Details
| Publisher | Packt Publishing |
| Publication date | December 31, 2018 |
| Language | English |
| Print length | 268 pages |
| ISBN-10 | 1789139902 |
| ISBN-13 | 978-1789139907 |
| Item Weight | 1.03 pounds (470 grams) |
| Dimensions | 7.5 x 0.61 x 9.25 inches (19.1 x 1.5 x 23.5 cm) |
Who Should Buy?
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Machine Learning Enthusiasts
Individuals interested in exploring advanced techniques and practical applications of generative models within AI and machine learning.
-
Data Scientists
Professionals looking to enhance their skill set in generative modeling and seeking practical solutions to real-world data challenges.
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Students & Researchers
Academics or learners aiming to deepen their understanding of GANs through hands-on recipes and coding examples in Python.
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Beginners in Coding
Novices with little programming experience may find the technical content and coding requirements overwhelming or confusing.
Product Description
Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras
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Neural Networks Editorial Review
The Generative Adversarial Networks Cookbook is a mixed bag, with one review describing it as full of nonsensical information and watery, and another praising it for breaking down code and providing references to original papers. The negative review criticises the author for wasting reader's time with unnecessary information and not providing useful code listings or clear figures. Overall, the review advises against buying the book. The positive review, however, recommends the book as a suitable starting point for those interested in GANs and highlights its value in understanding code and referencing original papers.
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Pros
- Suitable for beginners interested in GANs
- Helpful in understanding code and referencing original papers
Cons
- Contains nonsensical information and watery content
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