Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural networks are a type of deep learning, which is a type of machine learning. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and natural language processing. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and natural language processing.
application of deep neural classifier to fit a unbalanced dataset, expression datasets across the samples
demonstration of using AutoEncoders with r studio
Time series with torch
Analysis of simulated genotype alignment data for use in CNN estimators (Johnson and Wilke, 2022)
deep learning patient classifier based on patient similarity networks
Collection of machine learning algoritm written in R. covering various supervised and unsupervised machine learning algorithm. These codes are made as supplement of academic module in our data mining and knowledge management course.
The integration of machine learning and methodologies like deep learning will help greatly in classification of blood cells. Here i have implemented the deep learning model run with optimized hyperparameters against the other machine language methodologies like SVM and deep learning with defaults.The dataset for this project was taken from the K…
Research Project in the area of Deep Learning applied to credit risk. This model achieved better results compared to other published papers. This project is focused on the Fintech industry.
TAE Summer 2022 Data Competition
Web-application for 2D NMR peak picking and result visualisation using a Laplacian of Gaussian (LoG) and deep learning (TF Keras) approach - currently under development
R package for automatic hyper parameter tuning and ensembles with deep learning, gradient boosting machines, and random forests. Powered by h2o.
Flexible deep learning neural network. Implements a multi layer perceptron and autoencoders.
An R package which provides a a neural network framework based on Generalized Additive Models
Using Keras to build a deep neural network for bladder cancer progression
A classifier written in R which predicts whether a patient, diagnosed with "Hepatocellular Carcinoma", is likely to live or die within a year
Replication code for "RNN-based counterfactual prediction, with an application to homestead policy and public schooling"
Improved Mortality Forecasts using Artificial Intelligence.
Elman Deep Neural Network in Forecasting application using R programming tested on 3 types of dataset