Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Chapter 7
Transfer Learning
Spoilers
In this chapter, we will:
• learn about ImageNet and popular models like AlexNet, VGG, Inception, and
ResNet
• use transfer learning to classify images from the Rock Paper Scissors dataset
• load pre-trained models for fine-tuning and feature extraction
• understand the role of auxiliary classifiers in very deep architectures
• use 1x1 convolutions as a dimension-reduction layer
• build an inception module
• understand how batch normalization impacts model training in many ways
• understand the purpose of residual (skip) connections and build a residual
block
Jupyter Notebook
The Jupyter notebook corresponding to Chapter 7 [105] is part of the official Deep
Learning with PyTorch Step-by-Step repository on GitHub. You can also run it
directly in Google Colab [106] .
If you’re using a local installation, open your terminal or Anaconda prompt and
navigate to the PyTorchStepByStep folder you cloned from GitHub. Then, activate
the pytorchbook environment and run jupyter notebook:
$ conda activate pytorchbook
(pytorchbook)$ jupyter notebook
If you’re using Jupyter’s default settings, this link should open Chapter 7’s
notebook. If not, just click on Chapter07.ipynb in your Jupyter’s home page.
498 | Chapter 7: Transfer Learning