Tags: machine learning* + classification*

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  1. This article provides a comprehensive guide on the basics of BERT (Bidirectional Encoder Representations from Transformers) models. It covers the architecture, use cases, and practical implementations, helping readers understand how to leverage BERT for natural language processing tasks.

  2. This article provides a hands-on guide to classifying human activity using sensor data and machine learning. It covers preparing data, creating a feature extraction pipeline using TSFresh, training a machine learning classifier with scikit-learn, and validating the model using the Data Studio.

  3. A detailed guide on creating a text classification model with Hugging Face's transformer models, including setup, training, and evaluation steps.

  4. BEAL is a deep active learning method that uses Bayesian deep learning with dropout to infer the model’s posterior predictive distribution and introduces an expected confidence-based acquisition function to select uncertain samples. Experiments show that BEAL outperforms other active learning methods, requiring fewer labeled samples for efficient training.

  5. A tutorial on using LLM for text classification, addressing common challenges and providing practical tips to improve accuracy and usability.

  6. Replace traditional NLP approaches with prompt engineering and Large Language Models (LLMs) for Jira ticket text classification. A code sample walkthrough.

  7. Support Vector Machine (SVM) algorithm with a focus on classification tasks, using a simple 2D dataset for illustration. It explains key concepts like hard and soft margins, support vectors, kernel tricks, and optimization probles.

  8. A study investigating whether format restrictions like JSON or XML impact the performance of large language models (LLMs) in tasks like reasoning and domain knowledge comprehension.

  9. This article explores various metrics used to evaluate the performance of classification machine learning models, including precision, recall, F1-score, accuracy, and alert rate. It explains how these metrics are calculated and provides insights into their application in real-world scenarios, particularly in fraud detection.

  10. A Github Gist containing a Python script for text classification using the TxTail API

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