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Customizing KNN classifier with sklearn

The post describes using custom distance functions with KNeighborsClassifier in scikit-learn. It explains implementing Weighted K-Nearest Neighbors and creating a CustomKNN class, showcasing OOP principles while enhancing KNN functionality and evaluation accuracy.

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Vision-Language Models in Healthcare: Unlocking Multimodal Intelligence for Medical Applications

In summary: Vision-Language Models (VLMs) in healthcare represent a significant technological advancement, offering a promising pathway to integrate and analyze multimodal data, including medical imaging and textual reports, to improve diagnostic accuracy, treatment planning, and… Vision-Language Models in Healthcare: Unlocking Multimodal Intelligence for Medical Applications

Word Embeddings in PyTorch: A Complete Guide

The guide explains implementing, training, saving, and loading word embeddings in PyTorch. It details using nn.Embedding, a neural network model, and demonstrates applied code examples for each step.

WordPiece Tokenization: A Deep Dive

WordPiece Tokenization enhances classical tokenization strategies by breaking words into subwords to manage rare and out-of-vocabulary terms effectively, resulting in improved model performance and better language processing across diverse languages.

Forsterkningslæring i Robotikk og Spill

Forsterkningslæring er en maskinlæringsteknikk der en agent lærer gjennom interaksjon med sitt miljø for å maksimere belønninger. Denne metoden anvendes i robotikk, spillutvikling, økonomi og trafikkstyring for å optimere resultater.

ROCKET for time series classification: method & codes

ROCKET is an innovative time series classification method using random convolutional kernels for feature extraction. It performs efficiently, achieving state-of-the-art accuracy while being scalable to large datasets and real-time applications.

Example Outlines for the Related Works Section of a Paper

The “Related Works” section in machine learning papers contextualizes research, outlining themes, chronological developments, comparative analyses, and applications. This aggregation aids in identifying gaps, positioning contributions, and enhancing understanding of established methodologies.

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