Build logistic regression, neural network models for classification
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Updated
Jan 31, 2019
Build logistic regression, neural network models for classification
[BMVC'23 Oral] Offical repository of "Rethinking Transfer Learning for Medical Image Classification"
Source code for the numerical experiments presented in the paper "Greedy Shallow Networks: An Approach for Constructing and Training Neural Networks".
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
Predicting if a mushroom is edible or poisonous with a shallow neural network with Keras and TensorFlow 2.
Exploring "variability collapse" in shallow neural networks
Projects of "Bio Inspired Computing" Course - University of Tehran - Spring 2024
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
Car Price Prediction is a machine learning project aimed at developing a model that can predict the selling price of used cars based on various features or attributes.
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
In this project, we propose a cervical cancer detection and classification system using CNNs . We employ transfer learning and fine-tuning for enhanced performance. Classifiers like ELM and AE are added to increase the efficiency.
Credit Fraud Detection of a highly imbalanced dataset of 280k transactions. Multiple ML algorithms(LogisticReg, ShallowNeuralNetwork, RandomForest, SVM, GradientBoosting) are compared for prediction purposes.
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).
Using a shallow neural network in Retinopathy of Prematurity (ROP) image enhancement
Design of an one hidden layer neural network using numpy only,
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