Large Language Models

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Federated Learning in Computer Vision Explained

This article discusses how federated learning changes computer vision by training AI models without sharing raw data. It solves privacy issues and improves model accuracy, using examples like smartphones that are getting better at predicting text. We cover how federated learning works, its challenges, and how to solve them. Finally, we look at real-world uses in medical imaging, smart surveillance, self-driving cars, retail, farming, and smart home device

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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