Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
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Updated
Aug 6, 2024 - Jupyter Notebook
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.
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!
Code to train a custom time-domain autoencoder to dereverb audio
All course material and codes of Generative Adversarial Networks Specialization offered by DeepLearning.ai
Autoencoder for Feature Extraction
Image enhancement using GAN's and autoencoders
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
Columbia University Data Science Master Capstone Project. The goal of this project was to cluster trajectories by shape for later optimization.
An automatic adjustment model is developed for brightness adjustment in images.
Autoencoder-based Feature Selection for the SN_DREAMS diabetic retinopathy dataset. (With Prof. S. Raman)
A gentle introduction to autoencoders with examples
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