Pinned Repositories
aerial_pedestrian_detection
Applied-Plotting-Charting-Data-Representation-in-Python
ASHRAE-Kaggle-Competition-RadomForest
cdar
This R package will help researchers and practitioners in the area of construction engineering and management to conduct construction analytics including construction time series forecasting, construction project financing, and construction schedule optimization.
Coursera
NN and Deep
Data-Science-Mini-Project
A company provides a number of ways for its policyholders to make payments. While our service counselors can take payments over the phone, it is more cost-efficient for customers to make payments through our self-service channels such as online or through the automated phone system. Please use a predictive model to select people to receive a pre-emptive e-mail message designed to encourage them to pay online. You have been tasked with identifying which customers are likely to make a service payment call in the next 5 days. The attached file contains data on customers who have had a bill due in the next 5 days and whether they made a service payment call. Construct a model that predicts the likelihood that each policyholder will make a service payment call (CALL_FLAG=1). You may use whatever methods you see fit.
RandomForest-Test
skeyenet
Road and Building Segmentation in Satellite Imagery
Unsupervised-Machine-Learning-Dendrogram-Diagram
UrbanLandUse
Characterizing urban land use with machine learning
Bahram-Abediniangerabi's Repositories
Bahram-Abediniangerabi/cdar
This R package will help researchers and practitioners in the area of construction engineering and management to conduct construction analytics including construction time series forecasting, construction project financing, and construction schedule optimization.
Bahram-Abediniangerabi/Coursera
NN and Deep
Bahram-Abediniangerabi/RandomForest-Test
Bahram-Abediniangerabi/skeyenet
Road and Building Segmentation in Satellite Imagery
Bahram-Abediniangerabi/Unsupervised-Machine-Learning-Dendrogram-Diagram
Bahram-Abediniangerabi/UrbanLandUse
Characterizing urban land use with machine learning
Bahram-Abediniangerabi/aerial_pedestrian_detection
Bahram-Abediniangerabi/Applied-Machine-Learning-in-Python
Bahram-Abediniangerabi/Applied-Plotting-Charting-Data-Representation-in-Python
Bahram-Abediniangerabi/ASHRAE-Kaggle-Competition-RadomForest
Bahram-Abediniangerabi/Data-Science-Mini-Project
A company provides a number of ways for its policyholders to make payments. While our service counselors can take payments over the phone, it is more cost-efficient for customers to make payments through our self-service channels such as online or through the automated phone system. Please use a predictive model to select people to receive a pre-emptive e-mail message designed to encourage them to pay online. You have been tasked with identifying which customers are likely to make a service payment call in the next 5 days. The attached file contains data on customers who have had a bill due in the next 5 days and whether they made a service payment call. Construct a model that predicts the likelihood that each policyholder will make a service payment call (CALL_FLAG=1). You may use whatever methods you see fit.
Bahram-Abediniangerabi/ASHRAE-Kaggle-Competition
Bahram-Abediniangerabi/awesome-satellite-imagery-datasets
🛰️ List of satellite image training datasets with annotations for computer vision and deep learning
Bahram-Abediniangerabi/CNN-for-ASI
Tutorial: Convolutional Neural Networks for Automated Seismic Interpretation
Bahram-Abediniangerabi/fOptions
:exclamation: This is a read-only mirror of the CRAN R package repository. fOptions — Rmetrics - Pricing and Evaluating Basic Options. Homepage: http://www.rmetrics.org
Bahram-Abediniangerabi/HMS
Data for HMS Task
Bahram-Abediniangerabi/Incubator
Bahram-Abediniangerabi/junkyard
Collection of test files, probes, hacks and ideas
Bahram-Abediniangerabi/Mask_RCNN
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
Bahram-Abediniangerabi/PySIC
A package for satellite image classification using machine learning models
Bahram-Abediniangerabi/Satellite_Imagery_Analysis
Implementation of Machine Learning and Deep Learning techniques to find insights from the satellite data.
Bahram-Abediniangerabi/segmentation_models
Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
Bahram-Abediniangerabi/TA_Web
Bahram-Abediniangerabi/TwitterAnalytics_Web
Bahram-Abediniangerabi/USBuildingFootprints
Computer generated building footprints for the United States
Bahram-Abediniangerabi/visual-chatgpt
Official repo for the paper: Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
Bahram-Abediniangerabi/WaterDetect
Water Detect Algorithm