• Stars
    star
    4
  • Rank 3,221,063 (Top 65 %)
  • Language
    Python
  • License
    MIT License
  • Created almost 4 years ago
  • Updated almost 4 years ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Predicting House price using Artificial Neural Networks

More Repositories

1

image_to_handwriting_az

This project allows you to convert image into Azerbaijani handwriting
Python
33
star
2

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.
Python
24
star
3

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
Python
14
star
4

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.
Python
11
star
5

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.
Python
6
star
6

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
6
star
7

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.
5
star
8

advanced-hyperparameter-optimization-techniques

HalvingGridSearch, HalvingRandomSearch, Bayesian Optimization, Keras Tuner, Hyperband optimization
Jupyter Notebook
5
star
9

Scraper-Chatbot

Scraper chatbot which answer more than a half bilion questions.
Python
4
star
10

Easy-Recipes-bot

This telegram bot will find easy recipes in Azerbaijani using ingredients you already have in the kitchen.
Jupyter Notebook
4
star
11

experimenteer

Automate your classic machine learning experiments with experimenteer.
Python
3
star
12

session-based-recommender-supermarket

Session-based Recommendation System for Supermarket Context
Python
2
star
13

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

Generate images of clothing items by using Deep Convolutional Generative Adversarial Networks (DCGANs)
Python
2
star
14

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.
Python
2
star
15

mT5-based-azerbaijani-news-summarize

mT5-small based Azerbaijani News Summarization
2
star
16

Predict-Bike-Rental-Usage-with-ANNs

Predicting Bike Rental Usage by using Artificial Neural Networks (Regression task)
Python
2
star
17

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
2
star
18

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
2
star
19

Breast-Cancer-Classification

Predict whether the cancer is benign or malignant by using KNN
1
star
20

Taxi-v3

OpenAI's Taxi-v3 environment.
Python
1
star
21

Cleaning-Text-NLTK

Cleaning Text Manually and with NLTK.
Jupyter Notebook
1
star
22

Ensemble-Learning-Algorithms

Ensemble models in machine learning combine the decisions from multiple models to improve the overall performance
Python
1
star
23

Federated-Learning-for-News-Categorization

Federated Learning for News Categorization in Azerbaijani
Python
1
star
24

azerbaijani-medical-question-classification

Azerbaijani Medical Forum Question Classification
1
star
25

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
1
star
26

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.
Python
1
star
27

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
1
star
28

sesle-tts

"sΙ™slΙ™" converts text written in Azerbaijani language into speech. "sΙ™slΙ™" is built on the advanced VITS approach, recognized as one of the most advanced text-to-speech methods available today.
Python
1
star
29

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
1
star
30

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
1
star
31

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
1
star
32

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
1
star
33

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
1
star
34

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.
CSS
1
star
35

IMDB-Sentiment-Analysis

Sentiment Analysis using Recurrent Neural Network on 50,000 Movie Reviews Compiled from the IMDB Dataset
Python
1
star
36

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
1
star