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

Chapter 3
A Simple Classification Problem
Spoilers
In this chapter, we will:
• build a model for binary classification
• understand the concept of logits and how it is related to probabilities
• use binary cross-entropy loss to train a model
• use the loss function to handle imbalanced datasets
• understand the concepts of decision boundary and separability
• learn how the choice of a classification threshold impacts evaluation metrics
• build ROC and precision-recall curves
Jupyter Notebook
The Jupyter notebook corresponding to Chapter 3 [68] is part of the official Deep
Learning with PyTorch Step-by-Step repository on GitHub. You can also run it
directly in Google Colab [69] .
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 3’s
notebook. If not, just click on Chapter03.ipynb on your Jupyter’s home page.
Imports
For the sake of organization, all libraries needed throughout the code used in any
given chapter are imported at its very beginning. For this chapter, we’ll need the
following imports:
206 | Chapter 3: A Simple Classification Problem