There are 0 repository under l2-regularization topic.
A Deep Learning framework for CNNs and LSTMs from scratch, using NumPy.
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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
Фреймворк для построения нейронных сетей, комитетов, создания агентов с параллельными вычислениями.
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.
Analysis of the robustness of non-negative matrix factorization (NMF) techniques: L2-norm, L1-norm, and L2,1-norm
Modifiable neural network
Wrapper on top of liblinear-tools
Curso Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. Segundo curso del programa especializado Deep Learning. Este repositorio contiene todos los ejercicios resueltos. https://www.coursera.org/learn/neural-networks-deep-learning
Water and lipid signal removal in MRSI by L2 regularization (submitted by Liangjie Lin)
MITx - MicroMasters Program on Statistics and Data Science - Data Analysis: Statistical Modeling and Computation in Applications - Second Project
PyTorch implementation of important functions for WAIL and GMMIL
Simple Demo to show how L2 Regularization avoids overfitting in Deep Learning/Neural Networks
A simple python repository for developing perceptron based text mining involving dataset linguistics preprocessing for text classification and extracting similar text for a given query.
Repository for Assignment 1 for CS 725
Implementation of optimization and regularization algorithms in deep neural networks from scratch
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.
Machine Learning Course [ECE 501] - Spring 2023 - University of Tehran - Dr. A. Dehaqani, Dr. Tavassolipour
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.
Multivariate Regression and Classification Using a Feed-Forward Neural Network and Gradient Descent Optimization.
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.
Implementation of linear regression with L2 regularization (ridge regression) using numpy.
Deep Learning Course | Home Works | Spring 2021 | Dr. MohammadReza Mohammadi
Satellite imagery provides unique insights into various markets, including agriculture, defense and intelligence, energy, and finance. New commercial imagery providers, such as Planet, are using constellations of small satellites to capture images of the entire Earth every day. This flood of new imagery is outgrowing the ability for organizations to manually look at each image that gets captured, and there is a need for machine learning and computer vision algorithms to help automate the analysis process. The aim is to help address the difficult task of detecting the location of large ships in satellite images. Automating this process can be applied to many issues including monitoring port activity levels and supply chain analysis.
Image Classification with CNN using Tensorflow backend Keras on Fashion MNIST dataset
A framework for implementing convolutional neural networks and fully connected neural network.
Fully connected neural network with Adam optimizer, L2 regularization, Batch normalization, and Dropout using only numpy
Mathematical machine learning algorithm implementations
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.
The aim was to develop a robust Convolutional Neural Network (CNN) for accurately classifying handwritten digits from the MNIST dataset