Deep Learning With PyTorch

Deep learning with pytorch PDF, it is written by Authors Eli Stevens, Luca Antiga, Thomas Viehmann and published by Simon and Schuster in 2020 in high quality and in printable format, you can download it using direct link

Deep Learning With PyTorch PDF

PDF NameDeep Learning With PyTorch
Published/Updated On
Category
Primary RegionUnited States
No. of Pages522
PDF Size44.74 MB
LanguageEnglish
Source(s) / Creditsisip.piconepress.com

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Deep Learning With PyTorch PDF - Overview

Deep Learning With PyTorch in high quality, It is written by Authors Eli Stevens, Luca Antiga, Thomas Viehmann and published by Simon and Schuster in 2020. Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer.

Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Deep Learning with PyTorch will make that journey engaging and fun, It improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated.

Deep Learning With PyTorch

Contents of Deep Learning With PyTorch

  • Foreword
  • Preface
  • Acknowledgments
  • About This Book
  • About The Authors
  • About The Cover Illustration
  • Core Pytorch
  • Introducing Deep Learning And The Pytorch Library
  • Pretrained Networks
  • It Starts With A Tensor
  • Real-World Data Representation Using Tensors
  • The Mechanics Of Learning
  • Using A Neural Network To Fit The Data
  • Telling Birds From Airplanes: Learning From Images
  • Using Convolutions To Generalize
  • Using Pytorch To Fight Cancer
  • Combining Data Sources Into A Unified Dataset
  • Training A Classification Model To Detect Suspected Tumors
  • Improving Training With Metrics And Augmentation
  • Using Segmentation To Find Suspected Nodules
  • End-To-End Nodule Analysis, And Where To Go Next
  • Deploying To Production
  • Index

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