Project: Linear regression using pytorch

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.

Summary

In this tutorial, you discovered a step-by-step guide to developing deep learning models in PyTorch.

Specifically, you learned:

  • 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.

Junior @NITP🌍 ❯ Intern @Dataly ❯ Innovations Lead @dscnitp ❯ Projects Head @hackslash-nitp ❯ 🙅OSH Mentor @anitab-org ❯ ASI @alexa-dev-hub ❯ Mentor @OpenMined