The project explores multiple machine learning approaches including traditional ML models (Logistic Regression, SVM, Naive Bayes) and ensemble methods (Random Forest, XGBoost, Voting Classifier).

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...

Abstract: Various deep learning-based methods have greatly improved hyperspectral image (HSI) classification performance, but these models are sensitive to noisy training labels. Human annotation on ...

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 ...

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 ...