Efficient Astronomical Time transformations in Julia.

General pipeline used for analyzing EEG data where Raw EEG data gets transformed into ERPS and Stats are done in R (Mixed effects models)

Hand Gesture Recognition and Modification was based on transfer learning Inception v3 model using Keras with Tensorflow backend trained on 4 classes - rock, paper, scissors, and nothing hand signs. The final trained model resulted in an accuracy of 97.05%. The model was deployed using Streamlit on Heroku Paas.

Baby Health model made in Python.

Node modules and client utilities to build Persistence platform node applications.

PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS

scorEpochs: a computer aided scoring tool for resting-state M/EEG epochs

image classification and manipulation in python machine learning on fashion mnist dataset

As the learning rate is one of the most important hyper-parameters to tune for training convolutional neural networks. In this paper, a powerful technique to select a range of learning rates for a neural network that named cyclical learning rate was implemented with two different skewness degrees. It is an approach to adjust where the value is cycled between a lower bound and upper bound. CLR policies are computationally simpler and can avoid the computational expense of fine tuning with fixed learning rate. It is clearly shown that changing the learning rate during the training phase provides by far better results than fixed values with similar or even smaller number of epochs.

Gaze data -based epoch selection algorithm for eye tracker assisted visual evoked potential paradigm

The purpose of this project is to develop a machine learning model that predicts employee attrition (whether an employee will leave the company) and department assignment (which department an employee belongs to) based on various factors. These factors include age, travel frequency, education level, job satisfaction, marital status, and more.

Estimating evoked responses

This project creates a machine learning model that predicts the success of investing in a business venture.

A Generative Adversarial Network (GAN) that generates handwritten digits(0 to 9). Uses mnist dataset. Written in R

TensorFlow 2.2, Keras, Deep Learning

Glass Classification using Deep Learning Model ANN

Machine Learning -CNN

The main concentration of this project lies on image calssification using traditional CNN(Convolution Neural Networks), and also a couple of "BASE MODELS" such as "RestNet50", "DenseNet121" and "EfficientNetB0" that upgraded the performance of our CNN, followed by the Fully Connected NN, that we are using to train our model on.

Machine Learning and Neural Network techniques to recognize handwritten digits with high accuracy

Designing a deep learning neural network with TensorFlow to predict the success of donations

Neural_Network_Charity_Analysis

EEG data collection and processing in matlab. Proposed data collection algorithm and Processing pipeline for evoked potentials of EEG signals or regular EEG signals. Furthermore

PyTorch Library on Artificial Neural Network Model

This repository includes my Chest X-Ray Deep Learning-Flatiron School Module 4 Project. For this project, I made use of OS to access the data. The Pandas, NumPy, Matplotlib, Seaborn, and Plotly libraries were used to explore the data. Keras was used to build the image classifier.

This project involves training a machine learning model and plotting its learning curves to analyze training and testing accuracies, utilizing Java for model execution and Python for data visualization. It includes commands for compiling and running the Java program, generating plots, and sending results via email.

Module 21 Challenge

Image caption generator project is automatically describes images with coherent and relevant textual captions.

Scorepochs: a computer aided scoring tool for resting-state M/EEG epochs

A tool to print the eon, era, period, epoch, and age of a given positive whole number.

Scorepochs: a computer aided scoring tool for resting-state M/EEG epochs