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.
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!
Model Interpretability and Understanding for PyTorch using Captum
In this blog post, we examine Captum, which supplies academics and developers with cutting-edge techniques, such as Integrated Gradients, that make it simple to identify the elements that contribute to a model’s output. We then put these techniques to use in a coding demo with ResNet.
Multi-GPU on raw PyTorch with Hugging Face’s Accelerate library
In this article, we examine HuggingFace’s Accelerate library for multi-GPU deep learning. We apply Accelerate with PyTorch and show how it can be used to simplify transforming raw PyTorch into code that can be run on a distributed machine system.
Padding In Convolutional Neural Networks
In this article, we explore how and why we use padding in CNNs in computer vision tasks. We’ll then jump into a full coding demo showing the utility of padding.
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.
PyTorch 101: Understanding Hooks
We cover debugging and visualization in PyTorch. We explore PyTorch hooks, how to use them, visualize activations and modify gradients.
PyTorch Loss Functions
Follow this guide to learn about the various loss functions available to use with PyTorch.
How to use the PyTorch torch.max()
URL: https://www.progressiverobot.com/pytorch-torch-max/ In this article, we'll take a look at using the PyTorch torch.max() function. As you may expect, this is a very simple function, but interestingly, it has more than you imagine. Let's take a look at using this function, using some simple examples. NOTE: At the time of writing, the PyTorch version used […]
Comparing PyTorch and JAX
In this article, we look at PyTorch and JAX to compare and contrast their capabilities for developing Deep Learning models.
