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Farbod M.

@FarWebDigital

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AI Developer & Web Developer

$20 USD / Jam
・
Bendera
Canada (3:02 PG)
・
Menyertai pada Mei 16, 2021
$20 USD / Jam
・
Hello. I am Farbod. 5+ years of experience in (Machine Learning, Generative models, NLP, Big Data, API, and Back-End Development). Built end-to-end machine learning systems. ✅ Successfully Completed Projects: 1. Image Classification, Image segmentation, Image retrieval 2. Exploratory analysis and data visualization 3. Sentiment Analysis 4. Medical Image analysis 5. Question Answering System 6. Topic Modeling 7. Extract product specifications from product descriptions using GPT-3 8. GPT-3 Prompt design, GPT-3 prompt engineering, Fine-tuning GPT-3
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Lihat semua PHP Developers dalam Canada

Portfolio

Portfolio

11566667
Project description Deep Learning for Automatic Colorization of Grayscale Aerial Photographs using WGAN & Pix2Pix https://github.com/herodesigngit/Deep-Learning-for-Automatic-Colorization-of-Legacy-Grayscale-Aerial-Photographs-using-WGAN-Pix2Pix
Deep-Learning-for-Automatic-Colorization
11566663
Radar-based drone, car, people classification using transfer learning. https://github.com/herodesigngit/Radar-based-drone-classification
Radar-based drone, car, people classification using transfer
11566660
RFM, also known as RFM analysis, is a type of customer segmentation and behavioral targeting used to help businesses rank and segment customers based on the recency, frequency, and monetary value of a transaction. The shopping cart information of the customers of a chain store is available in an Excel file for a period of 1 year. In this project, analysis will be done on this information. This information includes all the variables that are recorded in each customer's purchase invoice, including customer name, product name, customer ID, product ID, product number, product amount, total invoice amount, store branch number, etc. Elbow diagram is used to determine the number of clusters and silhouette index is used to determine the quality of clustering. To determine the quality of the final clusters, ANOVA index is used, and then CLV based on Shannon's RFMC weighting method is calculated for each cluster. Next, CLV based on Shannon's RFMC weighting method is calculated for each cluster.
Customer segmentation using RFMC analysis
11566652
Project description Customer churn prediction for insurance company. This project is for an insurance company with real data and we are looking for a model to predict customer churn and model it. https://github.com/herodesigngit/Churn-Prediction
Customer churn prediction for insurance company
Project description Customer churn prediction for insurance company. This project is for an insurance company with real data and we are looking for a model to predict customer churn and model it. https://github.com/herodesigngit/Churn-Prediction
Customer churn prediction for insurance company
11566630
Role Research scientist and python programmer Project goal Increasing the performance of skin lesion classification system through optimizing hyper-parameters and architecture of deep neural network using metaheuristic optimization algorithms. Solution Three optimization algorithms are employed to find an optimal configuration for the convolutional neural network either in pre-trained models or model that are trained from scratch. Then the deep features extracted from the optimized models were fused together in pairs and used to train a KNN classifier.The effect of applying hyper-parameter optimization is evaluated on ISIC 2017 and ISIC 2018 datasets. The accuracy of the deep neural network produced by our method reaches to 81.6% and F1-score of 80.9% on ISIC 2017 dataset and accuracy of 90.1% and F1-score of 89.8% on ISIC 2018.
Hyper parameter optimization for skin lesion classification
Role Research scientist and python programmer Project goal Increasing the performance of skin lesion classification system through optimizing hyper-parameters and architecture of deep neural network using metaheuristic optimization algorithms. Solution Three optimization algorithms are employed to find an optimal configuration for the convolutional neural network either in pre-trained models or model that are trained from scratch. Then the deep features extracted from the optimized models were fused together in pairs and used to train a KNN classifier.The effect of applying hyper-parameter optimization is evaluated on ISIC 2017 and ISIC 2018 datasets. The accuracy of the deep neural network produced by our method reaches to 81.6% and F1-score of 80.9% on ISIC 2017 dataset and accuracy of 90.1% and F1-score of 89.8% on ISIC 2018.
Hyper parameter optimization for skin lesion classification
11566667
Project description Deep Learning for Automatic Colorization of Grayscale Aerial Photographs using WGAN & Pix2Pix https://github.com/herodesigngit/Deep-Learning-for-Automatic-Colorization-of-Legacy-Grayscale-Aerial-Photographs-using-WGAN-Pix2Pix
Deep-Learning-for-Automatic-Colorization
11566663
Radar-based drone, car, people classification using transfer learning. https://github.com/herodesigngit/Radar-based-drone-classification
Radar-based drone, car, people classification using transfer
11566660
RFM, also known as RFM analysis, is a type of customer segmentation and behavioral targeting used to help businesses rank and segment customers based on the recency, frequency, and monetary value of a transaction. The shopping cart information of the customers of a chain store is available in an Excel file for a period of 1 year. In this project, analysis will be done on this information. This information includes all the variables that are recorded in each customer's purchase invoice, including customer name, product name, customer ID, product ID, product number, product amount, total invoice amount, store branch number, etc. Elbow diagram is used to determine the number of clusters and silhouette index is used to determine the quality of clustering. To determine the quality of the final clusters, ANOVA index is used, and then CLV based on Shannon's RFMC weighting method is calculated for each cluster. Next, CLV based on Shannon's RFMC weighting method is calculated for each cluster.
Customer segmentation using RFMC analysis
11566652
Project description Customer churn prediction for insurance company. This project is for an insurance company with real data and we are looking for a model to predict customer churn and model it. https://github.com/herodesigngit/Churn-Prediction
Customer churn prediction for insurance company
Project description Customer churn prediction for insurance company. This project is for an insurance company with real data and we are looking for a model to predict customer churn and model it. https://github.com/herodesigngit/Churn-Prediction
Customer churn prediction for insurance company
11566630
Role Research scientist and python programmer Project goal Increasing the performance of skin lesion classification system through optimizing hyper-parameters and architecture of deep neural network using metaheuristic optimization algorithms. Solution Three optimization algorithms are employed to find an optimal configuration for the convolutional neural network either in pre-trained models or model that are trained from scratch. Then the deep features extracted from the optimized models were fused together in pairs and used to train a KNN classifier.The effect of applying hyper-parameter optimization is evaluated on ISIC 2017 and ISIC 2018 datasets. The accuracy of the deep neural network produced by our method reaches to 81.6% and F1-score of 80.9% on ISIC 2017 dataset and accuracy of 90.1% and F1-score of 89.8% on ISIC 2018.
Hyper parameter optimization for skin lesion classification
Role Research scientist and python programmer Project goal Increasing the performance of skin lesion classification system through optimizing hyper-parameters and architecture of deep neural network using metaheuristic optimization algorithms. Solution Three optimization algorithms are employed to find an optimal configuration for the convolutional neural network either in pre-trained models or model that are trained from scratch. Then the deep features extracted from the optimized models were fused together in pairs and used to train a KNN classifier.The effect of applying hyper-parameter optimization is evaluated on ISIC 2017 and ISIC 2018 datasets. The accuracy of the deep neural network produced by our method reaches to 81.6% and F1-score of 80.9% on ISIC 2017 dataset and accuracy of 90.1% and F1-score of 89.8% on ISIC 2018.
Hyper parameter optimization for skin lesion classification
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