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91676668
Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow
85% of respondents would recommend this to a friend
Nº de artículo:
91676668
CLP 88151
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With this book, you'll explore deep learning and learn how to put machine learning to use in your projects.
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What Stands Out
Comprehensive Coverage
This book provides extensive insights into various deep learning techniques and architectures, making it suitable for both beginners and advanced practitioners seeking to deepen their understanding of neural networks.
Practical Frameworks
Utilizing popular frameworks like PyTorch, Keras, and TensorFlow, readers gain hands-on experience in implementing deep learning models, addressing real-world problems with practical code examples and exercises.
Updated Content
The second edition includes the latest advancements in deep learning, ensuring readers are well-informed about current trends and techniques, enhancing their skills to stay competitive in the evolving tech landscape.
Detalles de producto
| Publisher | Packt Publishing |
| Publication date | January 16, 2019 |
| Edition | 2nd ed. |
| Language | English |
| Print length | 386 pages |
| ISBN-10 | 1789348463 |
| ISBN-13 | 978-1789348460 |
| Item Weight | 1.59 pounds (720 grams) |
| Dimensions | 7.5 x 0.87 x 9.25 inches (19.1 x 2.2 x 23.5 cm) |
Who Should Buy?
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Aspiring Data Scientists
Ideal for beginners wanting to build foundational skills in deep learning and neural networks using popular Python libraries.
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Machine Learning Developers
Well-suited for developers looking to deepen their understanding of deep learning techniques for practical machine learning applications.
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Academic Researchers
Beneficial for researchers seeking comprehensive insights into advanced neural network architectures and current industry frameworks.
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Complete Beginners
Not suitable for those with no programming or machine learning experience, as the material can be quite advanced.
DESCRIPCIÓN DEL PRODUCTO
Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow
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Intelligence & Semantics Editorial Review
**** The "Python Deep Learning - Second Edition" delivers a wide-ranging exploration into deep learning techniques and neural network architectures utilizing popular libraries like PyTorch, Keras, and TensorFlow. It covers crucial advancements in deep learning, effectively transitioning from foundational neural network concepts to more intricate topics such as convolutional and recurrent networks, reinforcement learning, and the practical applications of these methods in autonomous vehicles. Readers appreciate the book's comprehensive approach, which makes it beneficial for both beginners and seasoned practitioners looking to deepen their knowledge in specific areas like Generative Adversarial Networks (GANs) and natural language processing (NLP). The text provides a solid theoretical groundwork while also embedding practical examples that facilitate understanding, particularly in the sections concerning computer vision. However, the book is not without criticism. Some readers noted that while it includes a captivating introduction to reinforcement learning and an overview of AI's historical context, other sections, particularly in NLP, may feel rushed, lacking detailed explanations. Additionally, the second edition has received mixed reviews concerning its content compared to the first; some feel that valuable material has been omitted, leaving it feeling less definitive than its predecessor. Despite these criticisms, "Python Deep Learning - Second Edition" serves as an engaging entry point into deep learning for a broad audience and is recognized for its structured methodology and motivational content. **
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ventajas
- Comprehensive coverage of major deep learning topics.
- Effective for both beginners and experienced practitioners.
- Strong theoretical foundation blended with practical examples.
- Inclusion of multiple deep learning libraries encourages comparisons.
- Unique features such as chapters on reinforcement learning and autonomous vehicles.
Contras
- Some sections (e.g., NLP) may lack depth in explanations.
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CLP 88151
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características y beneficios
- Master advanced deep learning techniques with Python libraries.
- Build a strong foundation in neural networks.
- Explore applications in computer vision, NLP, and reinforcement learning.
- Learn to implement complex neural architectures like CNNs and LSTMs.
- Gain insights into generative models and state-of-the-art algorithms.
- Perfect for those with a basic knowledge of machine learning and Python.
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