Portfolio
Here are a few things I've been working on:
Here are some of the projects that I've completed using Python, NumPy, Pandas, Matplotlib, Seaborn, SciKit-Learn, and more. Projects feature data visualization and analysis as well as Machine Learning concepts including Long Short-Term Memory(LSTMs), KNN, Linear Regression, Logistic Regression, Support Vector Machines(SVMs), and more.
Looking for more? Head over to my GitHub repository.
Style Transfer: PyTorch
Image Style Transfer using PyTorch This project utilizes PyTorch, python, jupyter notebooks, matplotlib, and more to transfer the style of an image to the content of another image. This code can be applied to nearly any two images.
PyTorch Image Classifier
Image Classification with PyTorch. This deep learning project uses PyTorch to classify
images into 102 different species of flowers.
Project under construction.
imgAN : Training General Adversarial Networks
This Project is aimed at trining a GAN on human faces and attempts to generate
realistic human faces of people who don't actually exist!
Project uses PyTorch, Numpy, Matplotlib, Seaborn,
vectorization, image processing via pipeline, custom functions to leverage a GPU,
and train-test split.
Logistic Regression: Advertising
This logistic regression project attempts to predict whether an internet user will click
on an ad based on the features of the user.
Project uses Pandas, Numpy, Matplotlib,
Seaborn, and Scikitlearn.
THESIS : Prognostics Modelling of Turbofan Engines
Trained and Optimized a Prognostics model based on LSTM architecture and its variants to predict the engine life
and optimum maintenance time.
This project utilizes TensorFlow, Matplotlib, Keras, numpy, and Heatmaps.
Based on CMAPSS generated data.
MNIST
F1-score of 97.64%
Multi-Class / Multi-output Classification on MNIST Dataset.
Project uses Python, NumPy, Pandas, Matplotlib, and Scikit-Learn.
THESIS : Prognostics Modelling of Turbofan Engines
Trained and Optimized a Prognostics model based on LSTM architecture and its variants to predict the engine life
and optimum maintenance time.
This project utilizes TensorFlow, Matplotlib, Keras, numpy, and Heatmaps.
Based on CMAPSS generated data.