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)

Baby Health model made in Python.

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

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.

PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS

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

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

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 c…

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

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

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

TensorFlow 2.2, Keras, Deep Learning

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

Machine Learning -CNN

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

Neural_Network_Charity_Analysis

Module 21 Challenge

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.

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.