Wrapper on top of liblinear-tools

In this project, we aim to implement linear and polynomial regression of 2nd, 3rd, and 4th order from scratch, and apply L2-regularization to the 4th-order polynomial regression. We will perform these tasks using training data and evaluate the performance using different regularization parameters.

Implementing Ridge Regression (L2) to predict House Prices

The aim was to develop a robust Convolutional Neural Network (CNN) for accurately classifying handwritten digits from the MNIST dataset

The primary objective of this project is to design and train a deep neural network that can generalize well to new, unseen data, effectively distinguishing between rocks and metal cylinders based on the sonar chirp returns.

The point is to investigate three types of classifiers (linear classifier with feature selection, linear classifier without feature selection, and a non-linear classifier) in a setting where precision and interpretability may matter.

How much is the NBA dollar worth in terms of team success?

Multiclass Logistic, Classification Pipeline, Cross Validation, Gradient Descent, Regularization

The dataset that I am performing this regression analysis on, comes from Kaggle, titled crimes In India. This dataset holds complete information about various aspects of crimes that have taken place in India in a 17 year span, from 2001 to 2018.

Creating Neural Net from scratch using python , Numpy.

Machine Learning Course [ECE 501] - Spring 2023 - University of Tehran - Dr. A. Dehaqani, Dr. Tavassolipour

This repository contains the code for the blog post on Understanding L1 and L2 regularization in machine learning. For further details, please refer to this post.

A Deep Learning framework for CNNs and LSTMs from scratch, using NumPy.

To predict the energy generation of a wind turbine in Turkey, an energy model can be created using a multiple ridge regression (L2) function.

A study of the problem of overfitting in deep neural networks, how it can be detected, and prevented using the EMNIST dataset. This was done by performing experiments with depth and width, dropout, L1 & L2 regularization, and Maxout networks.

The aim was to create and implement a predictive model that can forecast the number of items sold for a period of 8 weeks ahead.

Used a Multilayer Perceptron (MLP) neural network to detect COVID-19 in lung scans.

linear regression with different types and datasets. Understanding of linear regression with Boston dataset using numpy.