Most machine learning workflows involve working with data, creating models, using hyperparameters to optimize model, saving and inferencing the trained models. This module introduces you to a complete machine learning (ML) workflow implemented in PyTorch, a popular ML framework for Python.

We’ll use the FashionMNIST dataset to train a neural network model that recognizes images such as: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, or Ankle boot.

Before we jump into building the model, you will learn key concepts required to understand the basics of building Neural Network models.

Learning objectives

In this module you will:

  • Learn how to use Tensors with CPUs and GPUs
  • Understand how to manage, scale and normalize your datasets
  • Build an image recognition model using a neural network
  • Learn how to optimize a model
  • Learn how to enhance model inference performance


  • Basic Python knowledge
  • Basic knowledge about how to use Jupyter Notebooks