Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.

Functional models and algorithms for sparse signal processing

L1-regularized least squares with PyTorch

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

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.

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…

An Image Reconstructor that applies fast proximal gradient method (FISTA) to the wavelet transform of an image using L1 and Total Variation (TV) regularizations

High Dimensional Portfolio Selection with Cardinality Constraints

MNIST Digit Prediction using Batch Normalization, Group Normalization, Layer Normalization and L1-L2 Regularizations

MITx - MicroMasters Program on Statistics and Data Science - Data Analysis: Statistical Modeling and Computation in Applications - Second Project

A wrapper for L1 trend filtering via primal-dual algorithm by Kwangmoo Koh, Seung-Jean Kim, and Stephen Boyd

Forecasting for AirQuality UCI dataset with Conjugate Gradient Artificial Neural Network based on Feature Selection L1 Regularized and Genetic Algorithm for Parameter Optimization

regression algorithm implementaion from scratch with python (least-squares, regularized LS, L1-regularized LS, robust 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.

Implementation of optimization and regularization algorithms in deep neural networks from scratch

Mathematical machine learning algorithm implementations

Comparing Three Penalized Least Squares Estimators: LASSO,SCAD and MCP.

Regularized regression using a forest fire data set

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.