l2-regularization
There are 75 repositories under l2-regularization topic.
indranil143/Mental-Health-Sentiment-Analysis-using-Deep-Learning
A deep learning project using fine-tuned RoBERTa to classify mental health sentiments from text, aiming to provide early insights and support. ⚕️❤️
sandipanpaul21/Logistic-regression-in-python
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-lans/NeuroRepository
Фреймворк для построения нейронных сетей, комитетов, создания агентов с параллельными вычислениями.
SSQ/Coursera-Ng-Improving-Deep-Neural-Networks-Hyperparameter-tuning-Regularization-and-Optimization
Short description for quick search
alejandrods/Analysis-of-the-robustness-of-NMF-algorithms
Analysis of the robustness of non-negative matrix factorization (NMF) techniques: L2-norm, L1-norm, and L2,1-norm
dedupeio/rlr
Regularized Logistic Regression
mansipatel2508/Network-Intrusion-Detection-with-Feature-Extraction-ML
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.
jamesneve/go-neural-network
Modifiable neural network
UnixJunkie/linwrap
Wrapper on top of liblinear-tools
mrshub/Wat_Lip_Removal_L2
Water and lipid signal removal in MRSI by L2 regularization (submitted by Liangjie Lin)
ArshTiwari2004/Sahyog
Centralized Disaster Response and Inventory Management System that leverages AI and Google Cloud Technologies to predict disasters, optimize resource management, and provide real-time coordination.
DunittMonagas/Improving-Deep-Neural-Networks-Hyperparameter-tuning-Regularization-and-Optimization
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
kinoute/Elyane
An OOP Deep Neural Network using a similar syntax as Keras with many hyper-parameters, optimizers and activation functions available.
sjsreehari/Heart-Risk-Detector-NeuralNetwork
Implemented a neural network from scratch in Python with just NumPy, no frameworks involved.
SapanaChaudhary/PyTorch-IL-functions
PyTorch implementation of important functions for WAIL and GMMIL
SawyerAlston/MNIST-NN-Pure-Math
A "from-scratch" 2-layer neural network for MNIST classification built in pure NumPy, featuring mini-batch gradient descent, momentum, L2 regularization, and evaluation tools — no ML libraries used.
bhattbhavesh91/regularization-neural-networks
Simple Demo to show how L2 Regularization avoids overfitting in Deep Learning/Neural Networks
KanishkNavale/Text-Mining-with-TF-IDF-and-Cosine-Similarity
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.
TwentyOneError/simple_neural_works
The module allows working with simple neural networks (Currently, the simplest model of a multilayer perceptron neural network with the backpropagation method and the Leaky ReLu activation function is used).
akshaykhadse/ml-linear-regression
Repository for Assignment 1 for CS 725
aliyzd95/Optimization-and-Regularization-from-scratch
Implementation of optimization and regularization algorithms in deep neural networks from scratch
bohaterewicz/Sales_forecasting_model
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.
fardinabbasi/Logistic_Regression
Implementing logistic regression with L2 regularization from scratch to classify circular datasets by mapping the feature space into higher dimensions.
federicoarenasl/Regularization-techniques-on-NNs
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.
gabrielegilardi/FeedForwardNN
Multivariate Regression and Classification Using a Feed-Forward Neural Network and Gradient Descent Optimization.
GhazaleZe/Investigate_Classifiers
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.
mmaric27/BasicDNN
Generic L-layer 'straight in Python' fully connected Neural Network implementation using numpy.
pwc2/ridge-regression
Implementation of linear regression with L2 regularization (ridge regression) using numpy.
saminheydarian/DeepLearning_Course_2021
Deep Learning Course | Home Works | Spring 2021 | Dr. MohammadReza Mohammadi
shouryasimha/Ships-In-Satellite-images
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.
sinturkgozde/Keras-CNN-Fashion-MNIST
Image Classification with CNN using Tensorflow backend Keras on Fashion MNIST dataset
SwikarGautam/ConvoNet
A framework for implementing convolutional neural networks and fully connected neural network.
SwikarGautam/DeepDenseNetwork
Fully connected neural network with Adam optimizer, L2 regularization, Batch normalization, and Dropout using only numpy
zhangyongheng78/Mathematical-Machine-Learning-Algorithm-Implementations
Mathematical machine learning algorithm implementations