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Deep Learning: Python for Data Science: A Guide to using Python for Tensor Flow, PyTorch, Keras in creating Deep Learning Model Frameworks Hardcover
€ 52
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Unlock the transformative power of deep learning with Deep Learning: Python for Data Science, your essential guide to building, training, and deploying advanced deep learning models using Python’s leading frameworks.
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| Item Weight | 2 lbs (910 grams) |
Who Should Buy?
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Aspiring Data Scientists
Provides essential knowledge and skills needed for anyone looking to enter the field of data science.
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Machine Learning Enthusiasts
Perfect for individuals who wish to deepen their understanding of deep learning frameworks and methods using Python.
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Tech Educators
Useful for instructors looking to update curricula with current deep learning framework practices and implementations.
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Complete Beginners
Not suitable for those without prior programming or data science knowledge, as it assumes foundational skills.
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Casual Readers
Individuals not seriously interested in deep learning may find the content too technical and dense.
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Non-Technical Professionals
Professionals outside tech fields might struggle with the technical jargon and concepts presented throughout the book.
OPIS PRODUKTU
Deep Learning: Python for Data Science: A Guide to using Python for Tensor Flow, PyTorch, Keras in creating Deep Learning Model Frameworks Hardcover – 19 May 2025
Otázky zákazníkov a odpovede na ne
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otázka:
What frameworks does the book cover?
odpoveď: The book covers TensorFlow, PyTorch, and Keras. -
otázka:
Is this book suitable for beginners?
odpoveď: Yes, it is suitable for both beginners and experienced developers. -
otázka:
What can I expect to learn from this book?
odpoveď: You will learn to build, train, and deploy advanced deep learning models.
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€ 52
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QTY:
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Funkcie a výhody
- Comprehensive guide to deep learning using Python.
- Covers TensorFlow, PyTorch, and Keras frameworks.
- Ideal for aspiring data scientists and experienced developers.
- Hands-on approach for building and deploying models.
- Includes advanced techniques for model architectures and data preprocessing.
- Offers practical insights for transitioning models into production.
