The project explores multiple machine learning approaches including traditional ML models (Logistic Regression, SVM, Naive Bayes) and ensemble methods (Random Forest, XGBoost, Voting Classifier).
Abstract: This study applies Bayesian learning techniques, specifically Variational Inference (VI) and Monte Carlo Dropout (MC Dropout) to Automatic Modulation Classification (AMC). Both methods are ...
ABSTRACT: Since transformer-based language models were introduced in 2017, they have been shown to be extraordinarily effective across a variety of NLP tasks including but not limited to language ...
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Hands-on coding of a multiclass neural network from scratch, with softmax and one-hot encoding. #Softmax #MulticlassClassification #PythonAI The 2 House Republicans who voted no on Trump's sweeping ...
This project is a simple spam message classifier built using Python's Scikit-learn library. It uses a Multinomial Naive Bayes model combined with a Count Vectorizer to classify text messages as either ...
Abstract: The key to whether artificial intelligence can play a transformative role in the field of education lies in whether it can effectively collaborate with educators and learners. For teachers, ...
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