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Counting shots, making strides: Zero, one and few-shot learning unleashed 

Data Science Dojo

In the dynamic field of artificial intelligence, traditional machine learning, reliant on extensive labeled datasets, has given way to transformative learning paradigms. Source: Photo by Hal Gatewood on Unsplash In this exploration, we navigate from the basics of supervised learning to the forefront of adaptive models.

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How to tackle lack of data: an overview on transfer learning

Data Science Blog

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves.

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Data Science Dojo - Untitled Article

Data Science Dojo

Counting Shots, Making Strides: Zero, One and Few-Shot Learning Unleashed In the dynamic field of artificial intelligence, traditional machine learning, reliant on extensive labeled datasets, has given way to transformative learning paradigms. Welcome to the frontier of machine learning innovation!

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AI 101: A beginner’s guide to the basics of artificial intelligence

Dataconomy

Understanding the basics of artificial intelligence Artificial intelligence is an interdisciplinary field of study that involves creating intelligent machines that can perform tasks that typically require human-like cognitive abilities such as learning, reasoning, and problem-solving.

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The Hidden Cost of Poor Training Data in Machine Learning: Why Quality Matters

How to Learn Machine Learning

The quality of your training data in Machine Learning (ML) can make or break your entire project. This article explores real-world cases where poor-quality data led to model failures, and what we can learn from these experiences. Machine learning algorithms rely heavily on the data they are trained on.

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Foundation models: a guide

Snorkel AI

Foundation Models (FMs), such as GPT-3 and Stable Diffusion, mark the beginning of a new era in machine learning and artificial intelligence. Foundation models are large AI models trained on enormous quantities of unlabeled data—usually through self-supervised learning. What is self-supervised learning?

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Explosion in 2017: Our Year in Review

Explosion

We founded Explosion in October 2016, so this was our first full calendar year in operation. In August 2016, Ines wrote a post on how AI developers could benefit from better tooling and more careful attention to interaction design. spaCy’s Machine Learning library for NLP in Python. Here’s what we got done.