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.
In part one of this tutorial, we show how AvatarCLIP works under the hood to generate and animate fine detailed figures with PyTorch, and end with a code demo for texturing and sculpting the initial model.
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 tutorial, we show how to construct the pix2pix generative adversarial from scratch in TensorFlow, and use it to apply image-to-image translation of satellite images to maps.
Learn how to write and implement AlexNet from scratch in Gradient!
In this tutorial, we look at various methodologies that facilitate and aid the interpretation of several computer vision models, including LIME, SHAP, Grad-CAM, Guided Grad-CAM, and Expected Gradients.
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.
Follow this tutorial to learn how GLIDE works and see how to implement it in a Gradient Notebook
