l2-regularization

A deep learning project using fine-tuned RoBERTa to classify mental health sentiments from text, aiming to provide early insights and support. ⚕️❤️

Фреймворк для построения нейронных сетей, комитетов, создания агентов с параллельными вычислениями.

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

Short description for quick search

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

Regularized Logistic Regression

Wrapper on top of liblinear-tools

Modifiable neural network

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.

Water and lipid signal removal in MRSI by L2 regularization (submitted by Liangjie Lin)

Implemented a neural network from scratch in Python with just NumPy, no frameworks involved.

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

An OOP Deep Neural Network using a similar syntax as Keras with many hyper-parameters, optimizers and activation functions available.

Logistic Regression implemented from scratch with gradient descent, evaluation metrics, and L2 regularization.

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.

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).

Simple Demo to show how L2 Regularization avoids overfitting in Deep Learning/Neural Networks

PyTorch implementation of important functions for WAIL and GMMIL