PyTorch Tutorial: How to Develop Deep Learning Models with Python
Last Updated on August 27, 2020
Predictive modeling with deep learning is a skill that modern developers need to know.
PyTorch is the premier open-source deep learning framework developed and maintained by Facebook.
At its core, PyTorch is a mathematical library that allows you to perform efficient computation and automatic differentiation on graph-based models. Achieving this directly is challenging, although thankfully, the modern PyTorch API provides classes and idioms that allow you to easily develop a suite of deep learning models.
In this tutorial, you will discover a step-by-step guide to developing deep learning models in PyTorch.
After completing this tutorial, you will know:
- The difference between Torch and PyTorch and how to install and confirm PyTorch is working.
- The five-step life-cycle of PyTorch models and how to define, fit, and evaluate models.
- How to develop PyTorch deep learning models for regression, classification, and predictive modeling tasks.
Let’s get started.
PyTorch Tutorial – How to Develop Deep Learning Models
Photo by Dimitry B., some rights reserved.