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 400

Supervised Learning Machine Learning Machine Learning Artificial Intelligence 400

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

Towards AI

JULY 23, 2023

In the first part of the series, we talked about how Transformer ended the sequence-to-sequence modeling era of Natural Language Processing and understanding. Semi-Supervised Sequence Learning As we all know, supervised learning has a drawback, as it requires a huge labeled dataset to train.

Natural Language Processing 98

Natural Language Processing Supervised Learning Deep Learning Deep Learning 98

DrivenData Labs

FEBRUARY 3, 2025

His research focuses on applying natural language processing techniques to extract information from unstructured clinical and medical texts, especially in low-resource settings. I love participating in various competitions involving deep learning, especially tasks involving natural language processing or LLMs.

Machine Learning 130

Machine Learning Machine Learning Data Science Natural Language Processing 130

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

IBM Journey to AI blog

AUGUST 11, 2023

Origins of the MLOps process MLOps was born out of the realization that ML lifecycle management was slow and difficult to scale for business application. A foundation model takes a massive quantity of data and using self-supervised learning and transfer learning can take that data to create models for a wide range of tasks.

Data Science 68

Data Science Machine Learning Machine Learning ML 68

Explosion

MAY 12, 2016

SyntaxNet provides an important module in a natural language processing (NLP) pipeline such as spaCy. For instance, we parsed every comment posted to Reddit in 2015, and used word2vec on the phrases, entities and words. It already did. But I definitely think there’s still much more to come. What’s next?

Natural Language Processing 40

Natural Language Processing Supervised Learning Algorithm 40