Deep Learning: Python for Data Science: A Guide to using Python for Tensor Flow, PyTorch, Keras in creating Deep Learning Model Frameworks Paperback – 19 May 2025
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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 Paperback – 19 May 2025
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.
Deep Learning: Python for Data Science: A Guide to using Python for Tensor Flow, PyTorch, Keras in creating Deep Learning Model Frameworks Paperback – 19 May 2025
№ на артикул: 138661975

Deep Learning: Python for Data Science: A Guide to using Python for Tensor Flow, PyTorch, Keras in creating Deep Learning Model Frameworks Paperback

№ на артикул: 138661975

€ 25

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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.
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What Stands Out

Comprehensive Frameworks
Covers major deep learning frameworks like TensorFlow, PyTorch, and Keras, providing a versatile toolkit for developers to create and customize models effectively.
User-Friendly Approach
Designed for data science enthusiasts, this guide simplifies complex concepts and offers practical examples to enhance learning and application in real-world scenarios.
Future-Proof Skills
Equips readers with essential skills in deep learning, ensuring relevance in the rapidly evolving tech landscape and enhancing career opportunities in data science.

Информация за продукта

Shop Deep Learning: Python for Data Science: A Guide to using Python for Tensor Flow, PyTorch, Keras in creating Deep Learning Model Frameworks Paperback – 19 May 2025 online at a best price in Bulgaria. B0F997FRZY
Item Weight2.5 lbs (1.13 kg)

Who Should Buy?

Suitable For
  • Aspiring Data Scientists

    Individuals looking to develop deep learning skills and utilize Python frameworks for data analysis and machine learning.

  • Python Developers

    Developers seeking to expand their knowledge into deep learning applications using popular libraries like TensorFlow and PyTorch.

  • Students Learning AI

    Students and learners interested in artificial intelligence who want a practical guide to implementing deep learning models.

Not Suitable For
  • Beginners in Programming

    Users with no programming experience may find the subject matter too complex and challenging to understand.

  • Occasional Learners

    Casual readers not intending to practice deep learning will not benefit from the practical coding examples provided.

  • Non-Technical Professionals

    Professionals outside of technical fields may struggle with the advanced terminology and concepts presented in the book.

ОПИСАНИЕ НА ПРОДУКТА

Deep Learning: Python for Data Science: A Guide to using Python for Tensor Flow, PyTorch, Keras in creating Deep Learning Model Frameworks Paperback – 19 May 2025

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Въпроси на клиенти и отговори

  • въпрос: Who is this book for?

    отговор: It's ideal for aspiring data scientists, experienced developers, and anyone interested in deep learning.
  • въпрос: What frameworks does the book cover?

    отговор: The book focuses on TensorFlow, PyTorch, and Keras.
  • въпрос: Can I use the techniques learned for real-world applications?

    отговор: Absolutely! The book provides practical strategies for building and deploying deep learning models in production.

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€ 25

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Характеристики и предимства

  • Comprehensive guide to deep learning using Python's top frameworks: TensorFlow, PyTorch, and Keras.
  • Hands-on approach tailored for aspiring data scientists, developers, and tech enthusiasts.
  • Master neural networks, model training, and advanced deep learning techniques.
  • Explore TensorFlow’s execution models, Keras's streamlined development, and PyTorch’s flexibility.
  • Learn to preprocess diverse data types and optimize model deployment for production.
  • Equipped with practical strategies for building AI-driven applications in today's world.