Forecasting for AirQuality UCI dataset with Conjugate Gradient Artificial Neural Network based on Feature Selection L1 Regularized and Genetic Algorithm for Parameter Optimization
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Jun 23, 2018 - Jupyter Notebook
Forecasting for AirQuality UCI dataset with Conjugate Gradient Artificial Neural Network based on Feature Selection L1 Regularized and Genetic Algorithm for Parameter Optimization
A wrapper for L1 trend filtering via primal-dual algorithm by Kwangmoo Koh, Seung-Jean Kim, and Stephen Boyd
Logistic regression with l1 and l2 regularization VS Linear SVM
Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.
The given information of network connection, model predicts if connection has some intrusion or not. Binary classification for good and bad type of the connection further converting to multi-class classification and most prominent is feature importance analysis.
Regularized regression using a forest fire data set
Overparameterization and overfitting are common concerns when designing and training deep neural networks. Network pruning is an effective strategy used to reduce or limit the network complexity, but often suffers from time and computational intensive procedures to identify the most important connections and best performing hyperparameters. We s…
2018-2019 Semester 1 at Soton, individual CW of ML
Comparision of Linear Regression, Ridge Regression, Lasso Regression
During this study we will explore the different regularisation methods that can be used to address the problem of overfitting in a given Neural Network architecture, using the balanced EMNIST dataset.
This repository is about machine learning algorithms
FashionMNIST - Logistic regression
MNIST Digit Prediction using Batch Normalization, Group Normalization, Layer Normalization and L1-L2 Regularizations
This is a mid-term project of Optimization Methods, a course of Institute of Data Science, National Cheng Kung University. This project aimed to construct the linear regression with L1 regularization and the logistic regression with L1 regularization.
Mathematical machine learning algorithm implementations
The project encompasses the statistical analysis of a high-dimensional data using different classification, feature selection, clustering and dimension reduction techniques.
Machine Learning Practical - Coursework 1 Report: 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.
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
Chapman University CS-510 Computing For Scientists Final Project
Classification Using Logistic Regression by Making a Neural Network Model. This project also includes comparison of Model performance when different regularization techniques are used
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