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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
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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
Artikal br.: 138661975

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

Artikal br.: 138661975

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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.

Detalji o proizvodu

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 Bosnia and Herzegovina. 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.

OPIS PROIZVODA

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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Pitanja i odgovori korisnika

  • Pitanje: Who is this book for?

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

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

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

AI & Machine Learning Editorial Review

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Karakteristike i prednosti

  • 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.