Deep Learning with PyTorch (Manning)
Wednesday, 26 August 2020

This book shows how to use PyTorch for deep learning projects.  Authors.Eli Stevens, Luca Antiga and Thomas Viehmann show how to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. After covering the basics, the book moves on to larger projects, and the centerpiece of the book is a neural network designed for cancer detection.

<ASIN:1617295264>

 

Author: Eli Stevens, Luca Antiga and Thomas Viehmann
Publisher: Manning
Date: August 2020
Pages: 520
ISBN: 978-617295263
Print: 1617295264
Audience: Python developers
Level: Intermediate
Category: Python and Artificial Intelligence

pytorchdeep

 

  • Training deep neural networks
  • Implementing modules and loss functions
  • Utilizing pretrained models from PyTorch Hub
  • Exploring code samples in Jupyter Notebooks

 

For recommendations of Python books see Books for Pythonistas and Python Books For Beginners in our Programmer's Bookshelf section.

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Author: Lucas da Costa
Publisher: Manning
Date: April 2021
Pages: 512
ISBN: 978-1617297915
Print: 1617297917
Audience: JavaScript developers
Level: Intermediate
Rating: 5
Reviewer: Ian Elliot
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Author: Ric Messier
Publisher: Wiley
Date: March 2021
Pages: 416
ISBN: 978-1119712978
Print: 1119712971
Kindle: B08WZ2D7WC
Audience: Developers wanting to learn Rust
Rating: 3
Reviewer: Mike James
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