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.
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 this article, we look at GPUs in depth to learn about memory bandwidth and how it affects the processing speed of the accelerator unit for deep learning and other pertinent computational tasks.
Follow this tutorial to master angle-based outlier detection (ABOD), and learn how to better optimize your datasets for deep learning.
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.
This article explores, suggests, and breaks down four popular techniques for augmenting data meant to be used with NLP.
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 article, we break down the differences between the capabilities of Gradient and Kaggle. Use this guide to pick the best platform for you!
In this article, we examine HuggingFace's Accelerate library for multi-GPU deep learning. We apply Accelerate with PyTorch and show how it can be used to simplify transforming raw PyTorch into code that can be run on a distributed machine system.
