Programming PyTorch for Deep Learning

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

eBook Details:

  • Paperback: 220 pages
  • Publisher: WOW! eBook; 1st edition (October 8, 2019)
  • Language: English
  • ISBN-10: 1492045357
  • ISBN-13: 978-1492045359

eBook Description:

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

Take the next steps toward mastering deep learning, the machine learning method that’s transforming the world around us by the second. In this practical Programming PyTorch for Deep Learning book, you’ll get up to speed on key ideas using Facebook’s open source PyTorch framework and gain the latest skills you need to create your very own neural networks.

  • Learn how to deploy deep learning models to production
  • Explore PyTorch use cases from several leading companies
  • Learn how to apply transfer learning to images
  • Apply cutting-edge NLP techniques using a model trained on Wikipedia
  • Use PyTorch’s torchaudio library to classify audio data with a convolutional-based model
  • Debug PyTorch models using TensorBoard and flame graphs
  • Deploy PyTorch applications in production in Docker containers and Kubernetes clusters running on Google Cloud

Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text, and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production.

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3 Responses

  1. February 14, 2020

    […] Beginning Anomaly Detection Using Python-Based Deep Learning: With Keras and PyTorch […]

  2. March 16, 2020

    […] Deep Learning with PyTorch Quick Start Guide: Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing […]

  3. March 17, 2020

    […] Applied Deep Learning with PyTorch takes your understanding of deep learning, its algorithms, and its applications to a higher level. The book begins by helping you browse through the basics of deep learning and PyTorch. Once you are well versed with the PyTorch syntax and capable of building a single-layer neural network, you will gradually learn to tackle more complex data problems by configuring and training a convolutional neural network (CNN) to perform image classification. As you progress through the chapters, you’ll discover how you can solve an NLP problem by implementing a recurrent neural network (RNN). […]

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