In this tutorial, we show how to apply model interpretability algorithms from Captum on simple models. We demo building a basic model and use attribution algorithms such as Integrated Gradients, Saliency, DeepLift, and NoiseTunnel to attribute the image's label to the input pixels and visualize it.
In this article we took a look at one of the uses of autoencoders: image denoising. In this tutorial, we show how an autoencoder's representation learning allows it to learn mappings efficient enough to fix incorrect pixels/datapoints.
In this article, we'll go over how to set up NLTK in a paperspace gradient and utilize it to carry out a variety of NLP operations during the text processing stage. Then, we will create a Keras model with the help of some NLTK tools for sentiment analysis text classification.
In this article, we look at the steps for creating and updating a container for the Stable Diffusion Web UI, detail how to deploy the Web UI with Gradient, and discuss the newer features from the Stable Diffusion Web UI that have been added to the application since our last update.
In this blogpost, we examined the architecture and capabilities of the Versatile Diffusion framework. We then demonstrated this model within a Gradient Notebook to perform txt2img, img2txt, image variation, text variation, dual-guided, and Latent Image to Text to Image synthesis.
Follow this guide to learn how to deploy your model with FastAPI using a Gradient Deployment. Readers should expect to learn how to upload their trained model as a Gradient model artifact, create a Docker image that will serve their model, and deploy their image on Gradient using a deployment.
In this article, we examine the processes of implementing training, undergoing validation, and obtaining accuracy metrics - theoretically explained at a high level. We then demonstrate them by combining all three processes in a class, and using them to train a convolutional neural network.
In this article, we discuss the process of conducting end-to-end data science on Gradient with Nvidia Merlin. This includes walkthroughs on 3 examples: Multi-stage recommenders, training and serving a MovieLens model, and scaling for the massive Criteo dataset.
In this article, we explore what dimensions imply in a convolutional neural network context. We show how to create a custom convnet as a baseline model, and then proceed to create new versions of it, with increased width, increased depth and the last one with both increased depth and width.
