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!
Follow this guide to learn how to integrate Arize within Gradient Deployments to monitor data drift, traffic, and other model monitoring metrics.
In this post, readers will see how to implement a decision transformer with OpenAI Gym on a Gradient Notebook to train a hopper-v3 "robot" to hop forward over a horizontal boundary as quickly as possible.
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.
Learn how to construct neural networks from scratch with NumPy, and simultaneously see how the internal mechanisms behind popular libraries like PyTorch and Keras are implemented.
This tutorial examines how to construct and make use of conditional generative adversarial networks using TensorFlow on a Gradient Notebook.
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.
