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.
Follow this guide to learn how to use built in and third party tools to monitor your GPU utilization with Deep Learning in real time.
These benchmarks show how the single GPU instances for Gradient Notebooks perform against one another in terms of cost, throughput, GPU memory, and more!
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.
We just released an enormous notebooks update with the addition of Gradient Datasets, support for interactive widgets, and improvements to cell, file, and kernel management.
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.
Paperspace and Hugging Face have partnered to provide free compute resources for a GAN-focused community sprint April 4 - April 15, 2022!
