Thursday, March 13, 2025
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MACHINE LEARN

A Leader’s Guide to Choosing The Right GenAI Platform

In a world where technology evolves at breakneck speed, cutting-edge innovations are reshaping how businesses operate and think. The latest advancements are not just...
MACHINE LEARN

Understanding SWAV: self-supervised learning with contrasting cluster assignments

Self-supervised learning aims to extract representation from unsupervised visual data and it’s super famous in computer vision nowadays. This article covers the SWAV method,...
MACHINE LEARN

What Can We Learn About the State of News Audiences in ANZ?

object(WP_Post)#7052 (24) { => int(35151) => string(2) "36" => string(19) "2024-10-16 22:29:38" => string(19) "2024-10-16 22:29:38" => string(10781) "It’s become a truism to state...
MACHINE LEARN

AI Trends Every Business Leader Should Know

Artificial intelligence is transforming the business landscape at a pace never witnessed before, and it is not over yet. As we enter 2024, staying...
MACHINE LEARN

Transformers in computer vision: ViT architectures, tips, tricks and improvements

You are probably already aware of the Vision Transformer (ViT). What came after its initial submission is the story of this blog-post. We will...
MACHINE LEARN

3D Medical image segmentation with transformers tutorial

Transformers are a big trend in computer vision. I recently gave an overview of some amazing advancements. This time I will use my re-implementation...
MACHINE LEARN

Neural Architecture Search (NAS): basic principles and different approaches

Neural Architecture Search (NAS) is the process of automating the design of neural networks’ topology in order to achieve the best performance on a...
MACHINE LEARN

Understanding Maximum Likelihood Estimation in Supervised Learning

This article demystifies the ML learning modeling process under the prism of statistics. We will understand how our assumptions on the data enable us...
MACHINE LEARN

Vision Language models: towards multi-modal deep learning

Multimodal learning refers to the process of learning representations from different types of modalities using the same model. Different modalities are characterized by different...
MACHINE LEARN

Self-supervised learning tutorial: Implementing SimCLR with pytorch lightning

In this hands-on tutorial, we will provide you with a reimplementation of SimCLR self-supervised learning method for pretraining robust feature extractors. This method is...
MACHINE LEARN

Build Apps with a Click

This blog post focuses on new features and improvements. For a comprehensive list, including bug fixes,...
MACHINE LEARN

How distributed training works in Pytorch: distributed data-parallel and mixed-precision training

In this tutorial, we will learn how to use nn.parallel.DistributedDataParallel for training our models in multiple GPUs. We will take a minimal example of...
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