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Build logistic regression, neural network models for 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 to model the toxic effects of chemical compounds. However, complexity-accuracy tradeoff still needs to be ac-counted in order to improve the efficiency and commercial deployment of these methods. In this study, we implement a hybrid framework consists of a shallow neural network and a decision classifier for toxicity prediction of chemicals that interrupt nuclear receptor (NR) and stress response (SR) signaling pathways. A model based on proposed hybrid framework is trained on Tox21 data using 2D chemical descriptors that are less multifarious in nature and easy to calcu-late. Our method achieved the highest accuracy of 0.847 AUC (area under the curve) using a shallow neural network with only one hidden layer consisted of 10 neurons. Furthermore, our hybrid model enabled us to elucidate the inter-pretation of most important descriptors responsible for NR and SR toxicity.
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
Human Data Analytics (Optional Project)
study of scene classification with different MLP layer types
Logistic Regression Implementations - ML, Shallow NN and Enhanced Deep Neural Network for Structured and Unstructured Data Classification
Deep learning Specialization on Coursera
Predicting if a mushroom is edible or poisonous with a shallow neural network with Keras and TensorFlow 2.
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
High-throughput detection and enumeration of tumor cells in blood using Digital Holographic Microscopy (DHM) and Deep Learning.
A Python-based Machine Learning repository for the purpose of developing and testing a type of Shallow Deep Networks.
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.
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.
Implementation of Deep Neural Networks
A shallow CNN model that is trained on X-ray chest images with preprocessing step of adaptive histogram equalization.
This is a classifier for classifying the planar data with one hidden layer.
Exploring "variability collapse" in shallow neural networks
Scene text detection on Indian scene text data using shallow version of YOLO v3 and v4
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.
Learning C and overoptimising
Challenge of shallow neural network approximation with one-dimensional input.
Design of an one hidden layer neural network using numpy only,