Deep Learning with PyTorch (Eli Stevens, et al)

 
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Deep Learning with PyTorch (Eli Stevens, et al)

We learn about new applications for deep learning every other day, like enhanced medical imaging, precise credit card fraud detection, long-range weather forecasting, and others.

You can control these superpowers with PyTorch. PyTorch streamlines deep learning without compromising sophisticated features and is instantly recognizable to anyone who is familiar with Python data tools like NumPy and Scikit-learn. It works well for constructing rapid models and is scalable easily from a laptop to an organization.

This book shows you how to use PyTorch to build deep learning and neural network systems. With the help of this useful book, you can start right away creating a tumor image classifier.

After going over the fundamentals, you'll discover best practices for the whole deep learning pipeline and take on increasingly challenging projects as your knowledge of PyTorch improves. In downloadable Jupyter notebooks, every code sample is accessible for exploration.

  • Understanding deep learning data structures such as tensors and neural networks
  • Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results
  • Implementing modules and loss functions
  • Utilizing pre-trained models from PyTorch Hub
  • Methods for training networks with limited inputs
  • Sifting through unreliable results to diagnose and fix problems in your neural network
  • Improve your results with augmented data, better model architecture, and fine-tuning

Ebook Details

About the Authors
  • Eli Stevens has held positions ranging from software engineer to CTO at firms in Silicon Valley.
  • Luca Antiga held positions as cofounder, CTO, and researcher (biomedical engineering) at an AI business.
  • Thomas Viehmann is a trainer, consultant, and core developer for PyTorch and machine learning.
Published
Published Date / Year
1st edition (August 4, 2020)
Permission
Free to read entire book online by the publisher (Manning), with limited time every day.
Hardcover
520 pages
eBook Format
HTML and PDF (522 pages)
Language
English
ISBN-10
1617295264
ISBN-13
978-1617295263

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