In this continuation on our series of writing DL models from scratch with PyTorch, we look at VGG. Follow this tutorial to learn how to create, train, and evaluate a VGG neural network for CIFAR-100 image classification
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.
Learn how to write and implement AlexNet from scratch in Gradient!
In this new tutorial, we will examine YOLOR object detection with PyTorch in detail to see how it combines implicit and explicit information with a unified representation. We then demonstrate how to use YOLOR with Gradient Notebooks.
Follow this tutorial to learn how GLIDE works and see how to implement it in a Gradient Notebook
Follow part 2 of this tutorial series to see how to train a classification model for object localization using CNN's and PyTorch. Then see how to save and convert the model to ONNX.
In part one of this series on object localization with pytorch, you will learn the theory behind object localization, and learn how to set up the dataset for the task.
Follow this guide to learn how to integrate the Weights and Biases API with your code in Gradient Notebooks! Readers should expect to learn how to get started with Weights and Biases, how to integrate it with Gradient, and how to log your training results in Weights and Biases via Gradient.
Follow this guide to learn about the various loss functions available to use with PyTorch neural networks, and see how you can directly implement a custom loss function in their stead.
