The Practical Guide to Advanced PyTorch
Master advanced PyTorch concepts. Learn efficient training, optimization techniques, custom models, and performance best practices.
How to use the PyTorch torch.max()
URL: https://www.progressiverobot.com/pytorch-torch-max/ In this article, we'll take a look at using the PyTorch torch.max() function. As you may expect, this is a very simple function, but interestingly, it has more than you imagine. Let's take a look at using this function, using some simple examples. NOTE: At the time of writing, the PyTorch version used […]
Regression vs Transformers: Key Differences and When to Use Each Model
Explore the differences between regression and transformer models in machine learning. Understand how each works and when to use them.
SAM 2: Meta’s Next-Gen Model for Video and Image Segmentation
In this article, we will explore SAM 2, which expands the capabilities of the original SAM to handle both images and videos. It excels in real-time object segmentation, enabling dynamic interaction through prompts and memory attention.
Stable Diffusion 3.5 Large with GPU cloud servers
In this article, we show how to use Stable Diffusion 3.5 Large image generation models with GPU cloud servers.
TOON vs. JSON
Overview of the benefits of Token Oriented Object Notation (TOON) for llm prompts and how to implement a TOON encoder in your workflow.
Understanding LLM Poisoning
Discover how LLM poisoning works, why even 0.01% poisoned data can compromise AI systems, and the steps to prevent backdoor attacks in models.
Visual Question Answering with Llama 3.2 HuggingFace
Learn to implement visual question answering with AI-driven image processing using Llama 3.2 Vision, integrated with the cloud provider’s cloud solutions.
Writing CNNs from Scratch in PyTorch
One of the best ways to learn about convolutional neural networks (CNNs) is to write one from scratch! In this post we look to use PyTorch and the CIFAR-10 dataset to create a new neural network.
Next Generation AI Development with the AMD MI300X
In this tutorial, we discuss the effectiveness of AMD GPUs for Deep Learning tasks. In particular, we focus on the powerful MI300X, now available for the cloud provider’s GPU Droplets, examine the specs of these potent machines in depth.
