Understanding dimensionality reduction in machine learning models


Data scientists use dimensionality reduction in machine learning models to remove irrelevant features from busy datasets.Read More

Related Articles

Understanding Transformers, the machine learning model behind GPT-3

You know that expression When you have a hammer, everything looks like a nail? Well, in machine learning, it seems like we really have discovered a magical hammer for which everything is, in fact, a nail, and they’re called Transformers. Transformers are models that can be designed to translate text, write poems and op eds, and even generate computer code. In fact, lots of the amazing research I write about on daleonai.com is built on Transformers, like AlphaFold 2, the model that predicts the structures of proteins from their genetic sequences, as well as powerful natural language processing (NLP) models like GPT-3, BERT, T5, Switch,…This story continues at The Next Web

What is semi-supervised machine learning?

Machine learning has proven to be very efficient at classifying images and other unstructured data, a task that is very difficult to handle with classic rule-based software. But before machine learning models can perform classification tasks, they need to be trained on a lot of annotated examples. Data annotation is a slow and manual process that requires humans to review training examples one by one and giving them their right labels. In fact, data annotation is such a vital part of machine learning that the growing popularity of the technology has given rise to a huge market for labeled data. From Amazon’s Mechanical Turk… This story continues at The Next Web

Your company’s AI strategy is failing — here are 3 reasons why

Most companies are struggling to develop working artificial intelligence strategies, according to a new survey by cloud services provider Rackspace Technology. The survey, which includes 1,870 organizations in a variety of industries, including manufacturing, finance, retail, government, and healthcare, shows that only 20% of companies have mature AI/machine learning initiatives. The rest are still trying to figure out how to make it work. There’s no questioning the promises of machine learning in nearly every sector. Lower costs, improved precision, better customer experience, and new features are some of the benefits of applying machine learning models to real-world applications. But machine learning is… This story continues at The Next Web

Responses

Your email address will not be published. Required fields are marked *

Receive the latest news

Subscribe To Our Weekly Newsletter

Get notified about chronicles from TreatMyBrand directly in your inbox