Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications
Ian Pointer is a data engineer specializing in machine learning solutions for Fortune 100 clients.
Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications
Nº de artículo: 19653510

Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications

Nº de artículo: 19653510

ARS 92503

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Ian Pointer is a data engineer specializing in machine learning solutions for Fortune 100 clients.
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What Stands Out

Comprehensive Guide
This book offers a thorough exploration of PyTorch, catering to both beginners and advanced developers, ensuring readers gain a solid foundation in deep learning principles and practices.
Practical Applications
Learn to create and deploy deep learning applications effectively, equipping readers with real-world skills and hands-on experience through practical examples and projects tailored for the modern tech landscape.
Accessible Format
Being a Kindle edition, the book provides an easily accessible learning resource, allowing readers to learn on-the-go with convenient navigation and interactive features for an enriched educational experience.

Detalles de producto

Learn how to create and deploy deep learning applications using PyTorch. Author Ian Pointer is a data engineer specializing in machine learning solutions. Shop at Ubuy Argentina
  • Master deep learning using Facebook's PyTorch framework
  • Learn to create neural networks for images, sound, and text
  • Understand transfer learning, model debugging, and production deployment
  • Explore PyTorch use cases from leading companies
  • Apply NLP techniques and torchaudio library for audio data classification
  • Deploy PyTorch applications in production using Docker and Kubernetes on Google Cloud
Publisher O'Reilly Media
Publication date October 29, 2019
Edition 1st
Language English
Print length 220 pages
ISBN-10 1492045357
ISBN-13 978-1492045359
Item Weight 2.31 pounds (1.05 kg)
Dimensions 7 x 0.5 x 9 inches (17.8 x 1.3 x 22.9 cm)

Who Should Buy?

Suitable For
  • Aspiring Data Scientists

    Perfect for learners seeking to build real-world deep learning skills using PyTorch for research or industry applications.

  • Software Engineers

    Ideal for engineers wanting to integrate deep learning capabilities into existing applications using PyTorch's robust framework.

  • Graduate Students

    Beneficial for graduate students in computer science or related fields focusing on deep learning projects and research.

Not Suitable For
  • Beginners in Programming

    Not suitable for users completely new to programming or coding, as it assumes prior knowledge of Python.

DESCRIPCIÓN DEL PRODUCTO

Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications

About This Item

Introducing "Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications 1st Edition" - the ultimate guide for mastering the world of deep learning and harnessing its power to build and deploy groundbreaking applications. Are you ready to take your understanding of Python deep learning to the next level? Look no further than this comprehensive guide that covers all the essential aspects of PyTorch - the leading deep learning framework that is revolutionizing the field of artificial intelligence and data analysis. With "Programming PyTorch for Deep Learning", you'll delve into a wealth of topics and gain the knowledge and skills to develop cutting-edge deep learning models, leverage the power of advanced algorithmic techniques, and apply them to a wide range of applications. Whether you're a seasoned programmer or a beginner looking to dive into the exciting world of deep learning, this book is designed to provide you with practical, hands-on examples that will enhance your understanding of the subject.

Get ready to create and deploy deep learning applications like never before! Key Features: - Python Deep Learning: Discover how to harness the power of Python to develop robust and scalable deep learning applications. - PyTorch Tutorials: Benefit from step-by-step tutorials that guide you through the process of building and training neural networks using PyTorch. - Deep Learning Models: Learn how to design and implement state-of-the-art deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). - Data Science and Analysis: Explore the essential techniques and tools for data science, including data analysis, visualization, and big data analytics. - Cloud Computing and High Performance Computing: Gain insights into cloud computing, distributed computing, and parallel processing to maximize the efficiency of your deep learning projects. - Comparative Analysis: Compare PyTorch with TensorFlow and discover the unique advantages offered by each framework. - AI Applications: Delve into real-world applications of deep learning in various domains, including finance, healthcare, retail, and marketing. Whether you're a data scientist, AI enthusiast, or aspiring deep learning professional, "Programming PyTorch for Deep Learning" is your ultimate guide to mastering the art of creating and deploying deep learning applications. Embrace the power of PyTorch and unlock the potential of artificial intelligence like never before. Get your copy now and embark on an exciting journey towards building the future of AI!.

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Neural Networks Editorial Review

The 1st Edition of "Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications" has received mixed customer feedback. Some readers found the book to be an excellent reference and a cohesive work that covers various advanced topics such as lablesmoothing, unet, resnet, transformer, etc., presenting everything in a well-thought-out manner. However, others found the book lacking in depth, containing errors, bugs, and outdated code, particularly in later chapters. Some mentioned that the code examples did not work without major issues, creating confusion and frustration. Additionally, there were concerns about directory references and missing files, causing difficulties in running the code.

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ventajas

  • Excellent reference for brushing up on project skills
  • Cohesively covers advanced topics in PyTorch and deep learning
  • Well-laid out for easy reading and references
  • Initial chapters contained useful tricks and working code examples

Contras

  • Contains errors, bugs, and outdated code

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