In this continuation on our series of writing DL models from scratch with PyTorch, we learn how to create, train, and evaluate a ResNet neural network for CIFAR-100 image classification.
Follow this tutorial to learn what attention in deep learning is, and why attention is so important in image classification tasks. We then follow up with a demo on implementing attention from scratch with VGG.
This article explores, suggests, and breaks down four popular techniques for augmenting data meant to be used with NLP.
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.
In this tutorial, we examine how the BERT language model works in detail before jumping into a coding demo. We then showed how to fine-tune the model for a particular text classification task.
This blog post details the concept of mixed precision training, its benefits, and how to implement it automatically with popular Deep Learning frameworks PyTorch and TensorFlow.
These benchmarks show how the single GPU instances for Gradient Notebooks perform against one another in terms of cost, throughput, GPU memory, and more!
This tutorial examines how to construct and make use of conditional generative adversarial networks using TensorFlow on a Gradient Notebook.
Follow our latest tutorial to see how to implement use Colossal AI with Gradient Notebooks to train a ResNet34 classifier on a multi-GPU machine.
