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Day 4 - PyTorch Basics: Building and Training a Neural Network

100 Days Of PyTorch

Learn how to use torch.nn to build the neural network structure, and how to train the model to recognize images, using a training and evaluation loop. It assumes you are already familiar with the theory behind neural networks (i.e loss functions, gradient descent).

May 11, 2025
Liam Groen

Day 3: Pytorch Basics - Transforms

100 Days Of PyTorch
Data comes in many different formats. On the other hand, PyTorch can only do machine learning with one data type, the tensor. Transforms can convert any data to a tensor. In…
May 10, 2025
Liam Groen

Day 2: PyTorch Basics - Dataset and DataLoader

100 Days Of PyTorch

In this post we learn about how to download datasets with PyTorch Datasets, and how to retrieve data from them for training an ML model while keeping data and model separate. We will see that working with these classes is pretty easy, and allows us to use all kinds of handy built-in methods.

May 9, 2025
Liam Groen

Day 1: Pytorch Basics - Tensors

100 Days Of PyTorch

Tensors lie at the heart of PyTorch. If we want to be proficient in PyTorch, we need to know what we can do with the Tensor object. There are over 1200 possible operations to perform on tensors, but only a few are needed to get started with PyTorch. In this post we learn what a Tensor is and how to perform basic operations with them. Familiarity with python programming is assumed.

May 8, 2025
Liam Groen

Backpropagation: How Does Your Computer Calculate Gradients?

explanation

How backpropagation is used to compute gradients in neural networks, using computational graphs and the chain rule.

Apr 20, 2025
Liam Groen
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