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jaychaontpuacc's Projects

closing-price-gan icon closing-price-gan

This repository contains a Generative Adversarial Network (GAN) model designed to predict the closing prices in the financial market. The GAN utilizes a combination of generator and discriminator networks to generate synthetic closing price data, which can be used for forecasting and analysis purposes.

creditcardfrauddetectiongan icon creditcardfrauddetectiongan

This project utilizes Generative Adversarial Networks (GANs) to tackle the problem of credit card fraud detection. GANs are a powerful deep learning technique that can be used for generating synthetic data, which can be beneficial in situations with imbalanced datasets, such as fraud detection.

cross-lingual-text-to-video-synthesis-of-government-press-releases icon cross-lingual-text-to-video-synthesis-of-government-press-releases

Text to Video of various PIB Press Releases using Artificial Intelligence / Machine Learning / Generative Adversarial Networks in English and 13 Regional Languages viz. Hindi, Urdu, Punjabi, Gujarati, Marathi, Telugu, Kannada, Malayalam, Tamil, Odia, Bengali, Assamese and Manipuri.

deepdow icon deepdow

Portfolio optimization with deep learning.

eal-gan icon eal-gan

The official codes for the paper Supervised Anomaly Detection via a Class-Balanced Generative Adversarial Network

f_anogan-pytorch icon f_anogan-pytorch

PyTorch unofficial implements `f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks ` paper.

fin-gan icon fin-gan

Code to accompany the paper "Fin-GAN: Forecasting and Classifying Financial Time Series via Generative Adversarial Networks"

fingan icon fingan

The implementation of "modeling financial time-series with generative adversarial networks"

fraud-detection-ml-system icon fraud-detection-ml-system

A Flask-based Fraud Detection ML System utilizes XGBoost for training and SMOTE for handling imbalanced data, achieving an F1 score of 0.92 on Kaggle dataset

gan-ad icon gan-ad

We used generative adversarial networks (GANs) to do anomaly detection for time series data.

gan-fintimeseries icon gan-fintimeseries

Generating synthetic financial time series using Generative Adversarial Networks built on the Transformer architecture.

gan_anomaly_detection icon gan_anomaly_detection

Implementation of a Generative Adversarial Network for anomaly detection task in (financial) time series data.

gandi icon gandi

Generative Adversarial Network for Detecting Irregularities. Applying GANs to anomaly detection.

keras-gan icon keras-gan

Keras implementations of Generative Adversarial Networks.

madegan icon madegan

A PyTorch implementation of the hierarchical deep learning model as outlined in the paper "Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals".

marketgan icon marketgan

Implementing a Generative Adversarial Network on the Stock Market

mts-cgan icon mts-cgan

Transformer-based Conditional Generative Adversarial Network for Multivariate Time Series Generation (IWTA - PAKDD2023)

multivariate-gan icon multivariate-gan

Public repository for article "Multivariate emulation of convective-scale numerical weather predictions with generative adversarial networks: a proof-of-concept"

popular-rl-algorithms icon popular-rl-algorithms

PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..

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