Data Science Dojo

DECEMBER 8, 2023

Zero-shot, one-shot, and few-shot learning are redefining how machines adapt and learn, promising a future where adaptability and generalization reach unprecedented levels. Source: Photo by Hal Gatewood on Unsplash In this exploration, we navigate from the basics of supervised learning to the forefront of adaptive models.

Supervised Learning 346

Supervised Learning Machine Learning Machine Learning Artificial Intelligence 346

Dataconomy

JUNE 23, 2017

To learn more about this topic, please consider attending our fourth annual PyData Berlin conference on June 30-July 2, 2017. The post How Faulty Data Breaks Your Machine Learning Process appeared first on Dataconomy. Miroslav Batchkarov and other experts will be giving talks.

Machine Learning 195

Machine Learning Machine Learning Data Science Natural Language Processing 195

Data Science Dojo

DECEMBER 8, 2023

Zero-shot, one-shot, and few-shot learning are redefining how machines adapt and learn, promising a future where adaptability and generalization reach unprecedented levels. Source: Photo by Hal Gatewood on Unsplash In this exploration, we navigate from the basics of supervised learning to the forefront of adaptive models.

Supervised Learning 195

Supervised Learning Machine Learning Machine Learning Artificial Intelligence 195

Hacker News

MARCH 25, 2022

First described in a 2017 paper from Google, transformers are among the newest and one of the most powerful classes of models invented to date. They’re driving a wave of advances in machine learning some have dubbed transformer AI. Now we see self-attention is a powerful, flexible tool for learning,” he added. “Now

Machine Learning 182

Machine Learning Machine Learning AI AI 182

Hacker News

MAY 3, 2023

The core process is a general technique known as self-supervised learning , a learning paradigm that leverages the inherent structure of the data itself to generate labels for training. Fine-tuning may involve further training the pre-trained model on a smaller, task-specific labeled dataset, using supervised learning.

Supervised Learning 181

Supervised Learning Natural Language Processing AI AI 181

Snorkel AI

MARCH 1, 2023

Foundation models are large AI models trained on enormous quantities of unlabeled data—usually through self-supervised learning. This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy.

Natural Language Processing 83

Natural Language Processing Supervised Learning Machine Learning Machine Learning 83

Applied Data Science

MARCH 11, 2022

In contrast to classification, a supervised learning paradigm, generation is most often done in an unsupervised manner: for example an autoencoder , in the form of a neural network, can capture the statistical properties of a dataset. Language as a game: the field of Emergent Communication Firstly, what is language?

AI 130

AI AI Data Science Machine Learning 130