Writing VGG from Scratch in PyTorch
We explore writing VGG from Scratch in PyTorch. Learn how to create, train, and evaluate a VGG neural network for CIFAR-100 image classification.
PyTorch 101: Learn Deep Learning with PyTorch
We dig deep into PyTorch’s functionality and cover advanced tasks such as using different learning rates, learning rate policies, and different weight initializations.
Comparing PyTorch and JAX
In this article, we look at PyTorch and JAX to compare and contrast their capabilities for developing Deep Learning models.
Deep Learning for Image Dehazing- The What, Why, and How
In this tutorial, we discuss the history of image dehazing, show how to set an image dehazing task up in a notebook, and then examine 7 different techniques for performing image dehazing with deep learning!
Introduction to Extractive and Abstractive Summarization Techniques
In this theory we cover the background theory behind a variety of methodologies for abstractive text summarization
Automatic Mixed Precision Using PyTorch
In this overview of Automatic Mixed Precision (AMP) training with PyTorch, we demonstrate how the technique works, walking step-by-step through the process of integrating AMP in code, and discuss more advanced applications of AMP techniques with code scaffolds to integrate your own code.
A Guide to the DataLoader Class and Abstractions in PyTorch
We will explore one of the biggest problems in the fields of Machine Learning and Deep Learning: the struggle of loading and handling different types of data.
How to Train A Question-Answering Machine Learning Model (BERT)
In this article, I will give a brief overview of BERT based QA models and show you how to train Bio-BERT to answer COVID-19 related questions from research papers.
