Deep Learning with Pytorch

Stevens, Eli, Antiga, Luca

  • 出版商: Manning
  • 出版日期: 2020-08-04
  • 售價: $1,700
  • 貴賓價: 9.5$1,615
  • 語言: 英文
  • 頁數: 450
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1617295264
  • ISBN-13: 9781617295263
  • 相關分類: 深度學習 DeepLearning

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商品描述

Every other day we hear about new ways to put deep learning to good use: 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 teaches you how to implement deep learning algorithms with Python and PyTorch. This book takes you into a fascinating case study: building an algorithm capable of detecting malignant lung tumors using CT scans. As the authors guide you through this real example, you'll discover just how effective and fun PyTorch can be.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

作者簡介

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.

目錄大綱

Part 1 Part 1. Core PyTorch
1 Introducing Deep Learning and the PyTorch Library
2 Pre-Trained Networks
3 It Starts with a Tensor
4 Real-World Data Representation Using Tensors
5 The Mechanics of Learning
6 Using A Neural Network To Fit the Data
7 Telling Birds from Airplanes: Learning from Images
8 Using Convolutions To Generalize


Part 2 Part 2. Learning from Images in the Real-World: Early Detection of Lung Cancer
9 Using PyTorch To Fight Cancer
10 Ready, Dataset, Go!
11 Training A Classification Model To Detect Suspected Tumors
12 Monitoring Metrics: Precision, Recall, and Pretty Pictures
13 Using Segmentation To Find Suspected Nodules
14 End-to-end nodule analysis, and where to go next


Part 3 Part 3. Deployment
15 Deploying to production