Build logistic regression, neural network models for classification
-
Updated
Jan 31, 2019
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…
Notebooks of programming assignments of Neural Networks and Deep Learning course of deeplearning.ai on coursera in August-2019
[BMVC'23 Oral] Offical repository of "Rethinking Transfer Learning for Medical Image Classification"
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
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.
Credit Fraud Detection of a highly imbalanced dataset of 280k transactions. Multiple ML algorithms(LogisticReg, ShallowNeuralNetwork, RandomForest, SVM, GradientBoosting) are compared for prediction purposes.
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
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).
Design of an one hidden layer neural network using numpy only,
Human Data Analytics (Optional Project)
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.
High-throughput detection and enumeration of tumor cells in blood using Digital Holographic Microscopy (DHM) and Deep Learning.
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.
Deep learning Specialization on Coursera
This is a classifier for classifying the planar data with one hidden layer.
Source code for the numerical experiments presented in the paper "Greedy Shallow Networks: An Approach for Constructing and Training Neural Networks".
Add a description, image, and links to the shallow-neural-network topic page so that developers can more easily learn about it.
To associate your repository with the shallow-neural-network topic, visit your repo's landing page and select "manage topics."