NijatZeynalov/Azerbaijani-Fake-News-Generator

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Python
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Apache License 2.0
Created over 4 years ago
Updated over 4 years ago

NijatZeynalov/Azerbaijani-Fake-News-Generator

NijatZeynalov

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The aim of this project is to generate fake news in the Azerbaijani language using LSTM Recurrent Neural Networks. LSTM Recurrent Neural Networks are powerful Deep Learning models which are used for learning sequenced data. Here a LSTM model was trained on 65 thousand samples, and it should be able to generate text.

image_to_handwriting_az

This project allows you to convert image into Azerbaijani handwriting

Satellite-images-to-real-maps-with-Deep-Learning

In this project, I developed a Pix2Pix generative adversarial network for image-to-image translation. I have used the so-called maps dataset used in the Pix2Pix paper.

New-product-demand-forecasting-via-Content-based-learning-for-multi-branch-stores

New product demand forecasting via Content based learning for multi-branch stores: Ali and Nino Use Case

mgpt-az-streamlit

This project is a Streamlit app that uses the mGPT-XL (1.3B) model to generate Azerbaijani text. Users can input partial text, and the model will complete it with contextually relevant text in Azerbaijani.

automate-web-scraping-send-whatsapp-alert-with-aws

Scraping Azerbaijani real estate website and sending whatsapp message with AWS Lambda function that automatically triggered by Amazon CloudWatch.

AzVoiceSent

AzVoiceSent is research project focused on sentiment classification from voice transcriptions in Azerbaijani. The project has the potential to provide valuable insights into the sentiment expressed by speakers in various domains and applications.

Norvig-s-Spell-Checker-Algorithm-for-Azerbaijani-Language

The purpose of this project is to prepare a spell checker for Azerbaijani language by implementing a Azerbaijani corpus to Norvig’s algorithm. The corpus I created consists of 1478667 words collected from 47 books in 6 fields (biology, geography, detective, literature, encyclopedia, novel)

Jupyter Notebook

Predict-House-Price-using-ANNs

Predicting House price using Artificial Neural Networks

Scraper-Chatbot

Scraper chatbot which answer more than a half bilion questions.

Easy-Recipes-bot

This telegram bot will find easy recipes in Azerbaijani using ingredients you already have in the kitchen.

Jupyter Notebook

advanced-hyperparameter-optimization-techniques

HalvingGridSearch, HalvingRandomSearch, Bayesian Optimization, Keras Tuner, Hyperband optimization

Jupyter Notebook

Federated-Learning-for-News-Categorization

Federated Learning for News Categorization in Azerbaijani

tweet-analysis-topic-modelling

In this project, I have explored the world of tweet analysis in the case of two European universities: University of Tartu and Lund University.

Jupyter Notebook

experimenteer

Automate your classic machine learning experiments with experimenteer.

KnowledgeGraph-MovieRecommender

This project demonstrates the creation and utilization of a knowledge graph to enhance movie recommendation systems.

session-based-recommender-supermarket

Session-based Recommendation System for Supermarket Context

Generate-Synthetic-Images-with-DCGANs-in-Keras

Generate images of clothing items by using Deep Convolutional Generative Adversarial Networks (DCGANs)

Reinforcement-Learning-TwoEnemies

In this game, I have used pygame which is a cross-platform set of Python modules designed for writing video games. Then, I have applied Deep Q-learning. We have two enemies in the game and one player trying to avoid these enemies.

mT5-based-azerbaijani-news-summarize

mT5-small based Azerbaijani News Summarization

Predict-Bike-Rental-Usage-with-ANNs

Predicting Bike Rental Usage by using Artificial Neural Networks (Regression task)

Detecting-Weapon-objects-by-using-RetinaNet-model-with-TensorFlow

I have used Object Detection API and retrain RetinaNet model to spot weapon objects using just 4 training images.

Jupyter Notebook

concept-drift-adversarial-validation

In the project, I have detected concept drift by using adversarial validation and Kolmogorov-Smirnov test which can also be used in the deployed system.

Jupyter Notebook

Serving-LLM-model-with-Ray-Serve

Breast-Cancer-Classification

Predict whether the cancer is benign or malignant by using KNN

Taxi-v3

OpenAI's Taxi-v3 environment.

Cleaning-Text-NLTK

Cleaning Text Manually and with NLTK.

Jupyter Notebook

Ensemble-Learning-Algorithms

Ensemble models in machine learning combine the decisions from multiple models to improve the overall performance

azerbaijani-medical-question-classification

Azerbaijani Medical Forum Question Classification

SuperMarket-Dataset

The dataset contains data on 438,826 Azerbaijani products purchased by 80,000 customers in 20 branches of the supermarket in 2019. You are able to download this dataset from my data.world account free of charge.

Jupyter Notebook

Building-Neural-Network-architectures-from-scratch

I have built simple versions of some Neural Network architectures (Alexnet, Inception-v1, Resnet-18, Vgg-16) from scratch by using TensorFlow.

Scraping-Rotten-Tomatoes

In this notebook, I have used scraping method for movies in the "Rotten Tomatoes" website. This project based on "Web Scraping and API Fundamentals in Python" course of 365 Data Science.

Jupyter Notebook

sesle-tts

"səslə" converts text written in Azerbaijani language into speech. "səslə" is built on the advanced VITS approach, recognized as one of the most advanced text-to-speech methods available today.

optuna-hyperparameter-optimization

Optuna is an open-source hyperparameter optimization framework to automate hyperparameter search. The key features of Optuna include automated search for optimal hyperparameters, efficiently search large spaces and prune unpromising trials for faster results, and parallelize hyperparameter searches over multiple threads or processes.

Jupyter Notebook

DEEP-LEARNING-FOR-SENTIMENT-ANALYSIS-ON-REVIEWS-OF-MODERN-AZERBAIJANI-MOVIES

The paper mainly describes the implementation of the Multilayer Perceptron (MLP) model - that can be used to detect sentiments from the text.

Jupyter Notebook

Tensorflow-MNIST-Exercises

These exercises are prepared by 365datascience.com for the "Deep Learning with TensorFlow 2.0" course. Exercises are based on MNIST dataset and consist of several main adjustments for trying and practicing Tensorflow.

Jupyter Notebook

fine-tuning-of-a-transformer-based-model-for-generating-trade-recommendations

This report details the implementation and fine-tuning of a transformer-based model for generating trade recommendations.

Jupyter Notebook

handling-imbalanced-data

An imbalanced classification problem is a problem that involves predicting a class label where the distribution of class labels in the training dataset is not equal.

Jupyter Notebook

SENTIMENT-ANALYZER-FOR-AZERBAIJANI-SENTENCES.

I have implemented Multi Layer Perceptron model to learn and predict the sentiment of sentence written in Azerbaijani. In order to perform this sentiment task, we use a mixture of baseline machine learning models and deep learning models to learn and predict the sentiment of binary reviews.

Virtual-Try-On-with-Diffusion-Models

With the increasing trend of online shopping, particularly in the fashion industry, there is a significant need to enhance the customer experience by providing realistic previews of clothing items.

IMDB-Sentiment-Analysis

Sentiment Analysis using Recurrent Neural Network on 50,000 Movie Reviews Compiled from the IMDB Dataset

Keras-Assignment

In this project, I have built a regression model using the deep learning Keras library, and then I have experiment with increasing the number of training epochs and changing number of hidden layers and you will see how changing these parameters impacts the performance of the model.

Jupyter Notebook