Hands-On Deep Learning with PyTorch

Developing image analysis apps, GAN-based networks, reinforcement learning algorithms and text engineering routines with Deep Learning PyTorch applications
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Hands-On Deep Learning with PyTorch

Sherin Thomas

Developing image analysis apps, GAN-based networks, reinforcement learning algorithms and text engineering routines with Deep Learning PyTorch applications
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Book Details

ISBN 139781788834131
Paperback304 pages

Book Description

Deep Learning is probably the fastest-growing, but also the most complex area of applied computing today. There are two major frameworks dominating the Deep Learning API landscape – Google’s TensorFlow, and Facebook’s PyTorch. Deriving from the open source Torch framework written in Lua, it was under the leadership of AI guru Yann LeCun that Pytorch developed into a major alternative.

PyTorch uses autodifferentiation to make it possible for developers to introduce new behaviors into their neural networks, without having to restart their networks. This is possibly the most important innovation for major machine and deep learning frameworks implemented in Pytorch. Also, PyTorch threads can run on CPUs as well as GPUs, providing major efficiency gains in the process.

This book shows us how to make the simplicity and power of Pytorch work for a Python developer. The first application we learn about is how how to process images using CNNs, but new algorithms like GANs and and natural language processing algorithms are introduced as well. The book ends with a chapter on reinforcement learning and how put PyTorch application into production

Table of Contents

What You Will Learn

  • Processing, improving and recognizing image features
  • Finding, interpreting and deriving insights from unstructured textual data
  • Learning several varieties of General Adversarial Networks (GANs)
  • Apply PyTorch implementations of reinforcement learning algorithms
  • Put PyTorch projects through a production cycle

Authors

Table of Contents

Book Details

ISBN 139781788834131
Paperback304 pages
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