Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.
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Updated
May 13, 2019 - Python
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
High Dimensional Portfolio Selection with Cardinality Constraints
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…
A wrapper for L1 trend filtering via primal-dual algorithm by Kwangmoo Koh, Seung-Jean Kim, and Stephen Boyd
MITx - MicroMasters Program on Statistics and Data Science - Data Analysis: Statistical Modeling and Computation in Applications - Second Project
Forecasting for AirQuality UCI dataset with Conjugate Gradient Artificial Neural Network based on Feature Selection L1 Regularized and Genetic Algorithm for Parameter Optimization
Logistic regression with l1 and l2 regularization VS Linear SVM
Classification Using Logistic Regression by Making a Neural Network Model. This project also includes comparison of Model performance when different regularization techniques are used
MNIST Digit Prediction using Batch Normalization, Group Normalization, Layer Normalization and L1-L2 Regularizations
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.
2018-2019 Semester 1 at Soton, individual CW of ML
House Price Analysis and Sales Price Prediction
Comparision of Linear Regression, Ridge Regression, Lasso Regression
regression algorithm implementaion from scratch with python (least-squares, regularized LS, L1-regularized LS, robust regression)
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.
This repository is about machine learning algorithms
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