Large Language Models

Get the latest insights on large language models: their advancements, applications, and transformative impact on AI.

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Beginners Guide to One-shot Learning

In the article, we discuss one-short learning, a computer vision model that uses only one example per data category instead of many to teach machine models. We go deeper to compare its counterparts and also check out its use cases.

The Complete Guide to Few-Shot Learning

Few-shot learning is a machine learning model that works with few labeled examples. The article describes how few-shot learning is used in various fields, such as natural language processing, computer vision, healthcare, and speech recognition. We outline different approaches, including meta-learning, data-level methods, parameter-level methods, generative techniques, and more that you need to check.

Understanding Model Drift In Machine Learning

In this guide, we'll explore different types of model drift, including concept and data drift, and discuss how to detect and tackle these issues. We'll also share some practical strategies for continuous retraining, model versioning, and monitoring performance metrics to keep your machine-learning models effective over time.

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