Build logistic regression, neural network models for classification

Source code for the numerical experiments presented in the paper "Greedy Shallow Networks: An Approach for Constructing and Training Neural Networks".

[BMVC'23 Oral] Offical repository of "Rethinking Transfer Learning for Medical Image Classification"

In recent times, toxicological classification of chemical compounds is considered to be a grand challenge for pharma-ceutical and environment regulators. Advancement in machine learning techniques enabled efficient toxicity predic-tion pipelines. Random forests (RF), support vector machines (SVM) and deep neural networks (DNN) are often ap-plied…

Libreria didattica per la creazione, addestramento e test di reti neurali fino a tre strati in linguaggio C

Notebooks of programming assignments of Neural Networks and Deep Learning course of deeplearning.ai on coursera in August-2019

Comparative Analysis of Activation Functions in Shallow Neural Networks for Multi-Class Image Classification Using MNIST Digits and CIFAR-10 Datasets with Fixed Architectural Parameters

Human Data Analytics (Optional Project)

Deep learning Specialization on Coursera

Predicting if a mushroom is edible or poisonous with a shallow neural network with Keras and TensorFlow 2.

Logistic Regression Implementations - ML, Shallow NN and Enhanced Deep Neural Network for Structured and Unstructured Data Classification

study of scene classification with different MLP layer types

Exploring "variability collapse" in shallow neural networks

A Python-based Machine Learning repository for the purpose of developing and testing a type of Shallow Deep Networks.

Challenge of shallow neural network approximation with one-dimensional input.

Implementation of DNN with Early Stopping from scratch in Python. Evaluation was done on two simple datasets (Blobs and Moons) and on one more challenging dataset (Fashion-MNIST).

Credit Fraud Detection of a highly imbalanced dataset of 280k transactions. Multiple ML algorithms(LogisticReg, ShallowNeuralNetwork, RandomForest, SVM, GradientBoosting) are compared for prediction purposes.

Design of an one hidden layer neural network using numpy only,

This project encompasses a range of neural and non-neural model implementations to classifiy MNIST digits. The goal is to compare the performance of each technique including details of hyper-parameters, training ans testing errors, training and testing duration and additional parameters used in the analysis.