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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 – 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 Hardcover – 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 using Python’s leading frameworks.
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
Item #: 138662018

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

Item #: 138662018

PKR 18032

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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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What Stands Out

Comprehensive Coverage
This guide offers in-depth insights into using Python with TensorFlow, PyTorch, and Keras, ensuring learners grasp deep learning frameworks effectively.
User-Friendly Approach
Designed for both beginners and advanced users, it simplifies complex concepts, making deep learning accessible to everyone wanting to harness Python.
Practical Applications
Includes hands-on projects and examples, allowing readers to immediately apply their knowledge and build real-world deep learning models.

Product Details

Shop 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 online at a best price in Pakistan. B0F993LDJL
Item Weight2 lbs (910 grams)

Who Should Buy?

Suitable For
  • Aspiring Data Scientists

    Provides essential knowledge and skills needed for anyone looking to enter the field of data science.

  • Machine Learning Enthusiasts

    Perfect for individuals who wish to deepen their understanding of deep learning frameworks and methods using Python.

  • Tech Educators

    Useful for instructors looking to update curricula with current deep learning framework practices and implementations.

Not Suitable For
  • Complete Beginners

    Not suitable for those without prior programming or data science knowledge, as it assumes foundational skills.

  • Casual Readers

    Individuals not seriously interested in deep learning may find the content too technical and dense.

  • Non-Technical Professionals

    Professionals outside tech fields might struggle with the technical jargon and concepts presented throughout the book.

Product Description

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

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Customer Questions & Answers

  • Question: What is 'Deep Learning: Python for Data Science' about?

    Answer: 'Deep Learning: Python for Data Science' is a guide that covers how to use Python with frameworks like TensorFlow, PyTorch, and Keras to create deep learning models. It focuses on practical applications and techniques for building machine learning algorithms and neural networks.
    Category: overview
    Confidence: high
  • Question: What are the prerequisites for reading this book?

    Answer: Readers should have a basic understanding of Python and familiarity with data science concepts. Knowledge of machine learning principles can be beneficial but is not required.
    Category: setup
    Confidence: high
  • Question: What frameworks does this guide discuss?

    Answer: 'Deep Learning: Python for Data Science' covers three major deep learning frameworks: TensorFlow, PyTorch, and Keras, providing insights into their use cases and functionalities.
    Category: usage
    Confidence: high
  • Question: Are there any specific software requirements for using the examples in the book?

    Answer: Yes, readers will need to install Python and the libraries associated with TensorFlow, PyTorch, and Keras to run the examples and exercises provided in the book. Detailed installation instructions are usually included in the text.
    Category: setup
    Confidence: high
  • Question: What can I expect to learn from this book?

    Answer: You can expect to learn how to implement various deep learning models using Python, understand the theory behind neural networks, and gain practical experience through hands-on projects and examples.
    Category: overview
    Confidence: high
  • Question: Is there any information on model optimization in this book?

    Answer: Yes, the book includes discussions and techniques on how to optimize deep learning models for performance, including tips on tuning hyperparameters and managing computational resources effectively.
    Category: usage
    Confidence: medium
  • Question: Does the book cover troubleshooting common issues faced in deep learning?

    Answer: Yes, it addresses common challenges and troubleshooting tips encountered in building and training deep learning models, helping readers resolve issues that may arise.
    Category: troubleshooting
    Confidence: medium
  • Question: What is the target audience for this book?

    Answer: The target audience includes data scientists, machine learning practitioners, and students who are looking to deepen their understanding of deep learning and its applications using Python.
    Category: overview
    Confidence: high
  • Question: Is there a chapter dedicated to Keras in this book?

    Answer: Yes, the book includes a dedicated section on Keras, explaining its features and demonstrating how to build various neural network architectures with it.
    Category: usage
    Confidence: high
  • Question: What are some key concepts covered in the book?

    Answer: Key concepts include neural network architecture, backpropagation, convolutional neural networks, recurrent neural networks, and model evaluation metrics, among others.
    Category: overview
    Confidence: high
  • Question: Are there practical projects included in the guide?

    Answer: Yes, the guide features practical projects that allow readers to apply what they've learned and build real-world applications using deep learning techniques.
    Category: usage
    Confidence: high
  • Question: What is the ideal learning path suggested by the book?

    Answer: The book suggests starting with basic Python programming concepts, followed by an introduction to machine learning, and then progressing to deep learning frameworks with practical examples and projects.
    Category: setup
    Confidence: medium
  • Question: Can this book be used as a reference for advanced deep learning topics?

    Answer: While it covers fundamental concepts, readers looking for advanced topics may need to supplement this book with additional resources specifically focused on those areas.
    Category: overview
    Confidence: medium
  • Question: Does the book discuss the ethical implications of AI and deep learning?

    Answer: The book may touch on ethical considerations, but detailed discussions on the ethical implications of AI and deep learning may not be a primary focus.
    Category: other
    Confidence: low

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PKR 18032

Order now and get it around Monday, June 29

QTY:

Secured transactionsecured transaction

Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.

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Features & Benefits

  • 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.