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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.

Regularized Logistic 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.

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

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

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.

PyTorch implementation of important functions for WAIL and GMMIL

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

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 organization…

A framework for implementing convolutional neural networks and fully connected neural network.

Deep Learning Course | Home Works | Spring 2021 | Dr. MohammadReza Mohammadi

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.

Multivariate Regression and Classification Using a Feed-Forward Neural Network and Gradient Descent Optimization.