James is an ML Engineer and Head of Engagement Marketing at Paperspace, who has spent 4 years as a Machine Learning marketing specialist. BSc in Psychology from University of St. Andrews, Scotland.
In this tutorial, we cover an introduction to diffusion modeling for image generation, examine the popular Stable Diffusion framework, and show how to implement the model on a Gradient Notebook.
In this tutorial, we examine the new YOLOv7 & its new features, learn how to prepare custom datasets for the model, and then build a YOLOv7 demo from scratch using NBA footage to detect and discern the ball handler from players on the court.
In this article, we break down the justification and inspiration for DALL-E Mini/Craiyon, explore its predecessors for comparison's sake, and implement the light image generator in Python code.
In this tutorial, we look in depth at Pix4Dmatic: the premiere software for photogrammetry at scale. Readers can expect to learn how to set up Pix4Dmatic on Core, walk through all the steps for staging a task, and then create a 3d model using provided sample data.
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 article, we break down the differences between the capabilities of Gradient and Kaggle. Use this guide to pick the best platform for you!
This blog breaks down the strengths and weaknesses of the Kaggle platform, lists the qualities a data scientist should seek in a ML ops platform, and suggests a number of alternatives to the readers to try out: Gradient, Colab, and Sagemaker.
Announcing new and expanded Ampere GPU availabilities on Paperspace!
This guide breaks down the capabilities of the Tensor Core technology used by the latest generations of Nvidia GPUs.
