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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
Deep Learning: Python for Data Science: A Guide to using Python for Tensor Flow, PyTorch, Keras in creating Deep Learning Model Frameworks
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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
Deep Learning: Python for Data Science: A Guide to using Python for Tensor Flow, PyTorch, Keras in creating Deep Learning Model Frameworks
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
Item #: 141578022

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

Item #: 141578022

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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 Python Guide
Offers an extensive overview of using Python tools like TensorFlow, PyTorch, and Keras, catering to data science enthusiasts looking to deepen their understanding of deep learning frameworks.
Practical Frameworks
Focuses on hands-on projects that demonstrate the creation and deployment of deep learning models, empowering users to apply theoretical knowledge in practical scenarios.
User-Friendly Approach
Designed for beginners and experienced developers alike, it simplifies complex concepts and encourages self-paced learning, making deep learning accessible to a broader audience.

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 online at a best price in Germany. B0F993LDJL
  • 8 x 10 Inch Large Pages - Color VersionDeep Learning: Python for Data Science A Guide to Using Python for TensorFlow, PyTorch, and Keras in Creating Deep Learning Model FrameworksUnlock 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—TensorFlow, PyTorch, and Keras. Whether you’re an aspiring data scientist, experienced developer, or technology enthusiast, this book provides a hands-on approach to mastering deep learning techniques crucial for today’s AI-driven world.Inside This Book:Foundations of Deep Learning: Understand neural networks, their evolution from traditional machine learning, and their applications in image recognition, natural language processing, and recommendation systems.Mastering TensorFlow: Dive into TensorFlow’s execution models, tensor operations, and data pipelines. Learn to build and optimize models, leverage GPU support, and enhance performance with tf.function.Harnessing Keras for Simplicity and Power: Utilize Keras to streamline model development. From Sequential models for quick prototypes to the Functional API for complex architectures, gain skills in compiling, training, evaluating, and visualizing models with TensorBoard.Exploring PyTorch’s Flexibility: Transition to PyTorch’s dynamic computation graph and intuitive design. Master tensor operations, define models with torch.nn.Module, implement automatic differentiation, and develop robust training scripts.Advanced Model Architectures: Enhance your models with multi-input/multi-output architectures, custom layers, and modules. Optimize training workflows with data augmentation, normalization, dropout, and batch normalization.Data Preprocessing and Input Pipelines: Effectively handle diverse data types—images, text, and tabular data. Convert raw data into tensors, utilize tf.data and torch.utils.data.Dataset for efficient data handling, and implement strategies for large-scale datasets.Deployment and Serving Models: Transition models to production with TensorFlow Serving and TorchServe. Create REST APIs using Flask or FastAPI, design batch inference pipelines, and optimize infrastructure for low latency and high throughput.For those interested in:zdeep learning, Python for data science, TensorFlow, PyTorch, Keras, neural networks, machine learning, AI models, data preprocessing, model training, model deployment, deep learning frameworks, advanced deep learning, data science guide, practical deep learning, building AI models, training neural networks, deploying deep learning models, TensorFlow tutorials, PyTorch tutorials, Keras tutorials, machine learning with Python, AI development, deep learning applications, image recognition, natural language processing, recommendation systems, GPU acceleration, TensorFlow vs PyTorch, Keras models, deep learning techniques, data science workflows, automated machine learning, deep learning projects, scalable AI models, deep learning optimization, model evaluation, deep learning pipelines, real-world AI, responsible AI, ethical deep learning, deep learning best practices, hands-on deep learning, comprehensive deep learning, multi-framework deep learning, deep learning strategies
Publisher Independently published
Publication date May 19, 2025
Language English
Print length 125 pages
ISBN-13 979-8284474990
Item Weight 13.4 ounces (379.89 grams)
Dimensions 8.25 x 0.49 x 11 inches (21 x 1.2 x 27.9 cm)

Who Should Buy?

Suitable For
  • Beginner Data Scientists

    Ideal for novices seeking to understand deep learning concepts and Python frameworks step-by-step.

  • Machine Learning Enthusiasts

    Great for those wanting to enhance their skills in deep learning with practical Python applications.

  • Developers Transitioning

    Helpful for software developers looking to transition into data science and deep learning environments.

Not Suitable For
  • Advanced Practitioners

    Not suitable for experienced deep learning professionals seeking high-level or cutting-edge research topics.

  • Casual Learners

    May not meet the needs of individuals seeking casual, non-technical deep learning overviews.

  • Non-Programmers

    Not recommended for those without programming knowledge, as the book requires Python understanding for effective learning.

Product Description

Have any Query? Chat with us

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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€ 52

Order now and get it around Sunday, July 05

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.