In this article, we will delve into the mechanics of monocular depth estimation, exploring the neural network architectures used, the techniques for training and improving these models, and the practical applications of this exciting field.
In part 2 of this tutorial series, we look at DETR's Hungarian Algorithm in depth to show how it minimizes cost.
DETR introduces a completely new architecture, setting a new standard in the object detection field. In this article, we explore the Detection Transformer (DETR) concept, highlighting its groundbreaking approach and the significant advancements it brings to object detection technology.
Tips for optimizing NLP models with backtracking algorithms, with coded examples.
Trying to decide between LlamaIndex and Langchain? Gain an overview and understand the key differences between the two most trending frameworks in the era of LLM.
In this article we will explore SkyPilot, a widely used framework designed to optimize GPU availability and minimize costs when running LLMs and AI on various cloud platforms.
In this deep dive, we show how to work with, pretrain, and finetune MosaicML models on Paperspace 8xH100 Machines.
In this tutorial, we continue looking at MAML optimization methods with the MNIST dataset.
In this tutorial, we look at Baidu's RT-DETR object detection framework, and show how to implement it in a Paperspace Notebook.
