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

Chapter 1
A Simple Regression Problem
Spoilers
In this chapter, we will:
• briefly review the steps of gradient descent (optional)
• use gradient descent to implement a linear regression in Numpy
• create tensors in PyTorch (finally!)
• understand the difference between CPU and GPU tensors
• understand PyTorch’s main feature, autograd, used to perform automatic
differentiation
• visualize the dynamic computation graph
• create a loss function
• define an optimizer
• implement our own model class
• implement nested and sequential models, using PyTorch’s layers
• organize our code into three parts: data preparation, model configuration, and
model training
Jupyter Notebook
The Jupyter notebook corresponding to Chapter 1 [39] is part of the official Deep
Learning with PyTorch Step-by-Step repository on GitHub. You can also run it
directly in Google Colab [40] .
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
60 | Chapter 1: A Simple Regression Problem