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183016583
Deep Learning with Python, Third Edition
88% of respondents would recommend this to a friend
Item #:
183016583
QAR 350
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Product Details
| Publisher | Manning |
| Publication date | November 18, 2025 |
| Edition | 3rd |
| Language | English |
| Print length | 648 pages |
| ISBN-10 | 1633436586 |
| ISBN-13 | 978-1633436589 |
| Item Weight | 1.58 pounds (720 grams) |
| Dimensions | 7.38 x 1.6 x 9.25 inches (18.7 x 4.1 x 23.5 cm) |
Product Description
Deep Learning with Python, Third Edition
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Neural Networks Editorial Review
**** The third edition of "Deep Learning with Python" by François Chollet has garnered widespread acclaim as an essential resource for anyone looking to delve into the world of AI and machine learning. Many readers have praised the book for its captivating and well-structured approach, making it the go-to manual for foundational knowledge in the field. From the onset, the text successfully explains fundamental concepts in machine learning and deep learning, laying a solid groundwork before venturing into more complex themes from Chapter 12 onwards. This chapter and those that follow introduce new material reflective of advancements in technology up until the mid-to-late 2020s, including critical discussions on object detection and large language models (LLMs). The blend of clear explanations and practical, executable Python code creates an enriching experience, appealing to both novices and experienced developers. Readers have pointed out the author's effective teaching methods that combine theoretical understanding with real-world methodologies. As noted, the book does not shy away from in-depth best practices, making it particularly beneficial for beginners entering the field. The comprehensive range of topics allows readers to access essential subjects like text classification, diffusion models, and generative AI methods all in one volume. Additionally, the choice of coding examples across popular frameworks such as TensorFlow, JAX, and PyTorch aligns well with current industry practices. This versatility ensures the material is not only informative but also relevant in today’s evolving tech landscape. The quality of the printing and digital formats has been highlighted as well, further enhancing the overall reading experience. Overall, this edition stands out as an indispensable tool for developers and learners alike, merging insightful content with hands-on applications, confirming its position as a top resource in the realm of machine learning literature. **
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Pros
- Well-structured and captivating content.
- Clear explanations of fundamental and advanced topics.
- Practical examples with Python code facilitate learning.
- Comprehensive coverage of major themes, including recent advancements in technology.
- Includes coding examples in popular frameworks such as TensorFlow, JAX, and PyTorch.
- Engaging writing style makes complex concepts accessible.
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