Introduction
Most machine-learning workflows involve working with data, creating models, using hyperparameters to optimize models, and saving and then 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 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, we show you the key concepts for building Neural Network models.
Learning objectives
In this module, you'll:
- 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.
Prerequisites
- Basic Python knowledge
- Basic knowledge about how to use Jupyter Notebooks