Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems

Detecting malicious URLs using an autoencoder neural network

Geometric Dynamic Variational Autoencoders (GD-VAEs) for learning embedding maps for nonlinear dynamics into general latent spaces. This includes methods for standard latent spaces or manifold latent spaces with specified geometry and topology. The manifold latent spaces can be based on analytic expressions or general point cloud representations.

Training Deep AutoEncoders for Collaborative Filtering

Using convolutional autoencoders to remove random noise from seismic data.

Code to train a custom time-domain autoencoder to dereverb audio

Use auto encoder feature extraction to facilitate classification model prediction accuracy using gradient boosting models

Micro neural network with multi-dimensional layers, multi-shaped data, fully or locally meshing, conv2D, unconv2D, Qlearning, ... for test!

All course material and codes of Generative Adversarial Networks Specialization offered by DeepLearning.ai

Deep learning fraud detection system using MLP, Autoencoder, and VAE for imbalanced credit card data. Built with PyTorch, it includes SMOTE, RobustScaler preprocessing, FastAPI REST API for real-time predictions, and an interactive dashboard. Features EDA, ROC-AUC/PR-AUC evaluation, and unit tests.

The Credit Card Fraud Detection System is a web-based machine learning application designed to analyze online financial transactions and detect potentially fraudulent activities. Built with Streamlit, TensorFlow, and Python, the system leverages an Autoencoder deep learning model trained on large-scale transaction data to identify abnormal transac

An automatic adjustment model is developed for brightness adjustment in images.

Image enhancement using GAN's and autoencoders

NASA Bearing Dataset: Fault Detection with Wiener denoising and custom time-frequency btstft Transforms

Autoencoder for Feature Extraction

Anomaly detection (also known as outlier analysis) is a data mining step that detects data points, events, and/or observations that differ from the expected behavior of a dataset. A typical data might reveal significant situations, such as a technical fault, or prospective possibilities, such as a shift in consumer behavior.

Gemerator is an autoencoder based mixed gem image generator, also it has a website and web service written in Django and Flask and deployed using PythonAnywhere and Google Cloud, Respectively

Gaussian Latent Dirichlet Allocation

Bias field correction for T-1 weighted MRI images for tumor detection

This is my academic thesis work (individual). Submitted in partial fulfilment of the requirements for Degree of Bachelor of Science in Computer Science & Engineering