Implementation of Trust Region and Gradient Descent methods for Multinomial Regression
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Updated
May 15, 2024 - Jupyter Notebook
Implementation of Trust Region and Gradient Descent methods for Multinomial Regression
Machine Learning course instructed by Dr. Riahi, Fall 2023, Shahid Beheshti University
Image prediction model with logistic regression multiclass model, ML library as sklearn, Matplotlib using Python.
Multiclass Logistic, Classification Pipeline, Cross Validation, Gradient Descent, Regularization
We investigated the performance of the Logistic and Multiclass Regression models and compared their accuracies to KNN. We compared Logistic Regression and KNN based on the "IMdB reviews" dataset, while Multiclass Regression and KNN were compared based on the "20 news groups" dataset.
💵Model Peruvian Bills (MLR, Mask, Inceptionv2) RCNN💶
Investigated how multi-class logistic regression would perform if the activation function was changed from softmax to sigmoid. It included mathematical analysis and empirical evaluation, such as rewriting the model from scratch. Tech: Python (Scikit-Learn, Pandas)
Here we built a multinomial logistic regression classifier with scikit-learn. It takes numerical data of a bean an predicts which class does it belong to.
Image Classification, Image Feature Extraction, CNNs, Finetuning, Resnet18, Torchvision, Multi-Class Logistic Regression
Multiclass Logistic, Classification Pipeline, Cross Validation, Gradient Descent, Regularization
- Built a deep learning model from scratch, used the mathematical equations to implement algorithms in the model
Employee Task management and review system for EinNell Expound Hackathon 2019
Machine Learning Predictive model - Logistic Regression on the quality of red wine
Predicting gender of given Chinese names (93~99% test set accuracy). 预测中文姓名的性别(93~99%的测试集准确率)。
The project focuses on sentiment analysis of Coronavirus tweets NLP - Text Classification kaggle dataset
Handwritten Digit Recognition by 2 methods: - Multi-class classification (oneVsAll) - Neural Network ---- OCTAVE -- the exercise details are in ex3.pdf
Logistic Regression technique in machine learning both theory and code in Python. Includes topics from Assumptions, Multi Class Classifications, Regularization (l1 and l2), Weight of Evidence and Information Value
Logistic Regression is a supervised learning algorithm that is used when the target variable is categorical. In Logistic Regression the target variable is categorical where we have to strict the range of predicted values. Consider a classification problem, where we need to classify whether an email is a spam or not. So we have to predict either …
A multi-class classification problem where the objective is to read a question posted on the popular reference website, StackOverflow and predict the primary topics it deals with, i.e. tags which the question will be associated with.
A multi class persian text classification using logistic regression
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