Alexander Mulet (@amulet1989)
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amulet1989

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1

Microservices-architecture-using-Docker-for-Image-classification-with-CNN

This project develops a solution that can automatically classify images into over 1000 different categories using a Convolutional Neural Network (CNN) implemented in Tensorflow. Our solution consists of a Web UI and a Python Flask API that serves the CNN. The Web UI allows users to upload an image and receive the predicted class for that image.
Python
4
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2

GBManalizer

This is an algorithm developed in Matlab to perform fully automatic segmentation of human Glioblastoma multiforme in Magnetic Resonance images. Pre-processed T1, T1C, T2 and Flair MRI modalities are required as described in the BraTS 2020 schedule.
MATLAB
3
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3

Redis_producer-consumer_example

You can see this code example to understand how to use Redis message queue and hash table to communicate two services.
Jupyter Notebook
2
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4

GBManalizerApp

Glioblastoma multiforme (GBM) is an aggressive brain tumor with a poor prognosis and high recurrence rate. GBManalizer, a Windows application, is introduced to automatically segment GBM and its components in magnetic resonance imaging (MRI) images. It utilizes preprocessing techniques and a neural network to achieve 83.9% accuracy.
MATLAB
2
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5

Financial-Advisor-Chatbot-using-chat-conversational-react-description-agent

In this project, we created a chatbot acting as a financial advisor enable to answer questions related to public companies listed in NASDAQ. The bot uses a conversational Agent to coordinate chains of thoughts inserted in ChatGPT through its API, and has access to a database with around ~10.000 public financial documents.
Jupyter Notebook
2
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6

Vehicle-classification-from-images

This is a Multi-class Classification task: we want to predict, given a picture of a vehicle, which of the possible 25 classes is the correct vehicle make-model.
Jupyter Notebook
2
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7

Sentiment-Analysis-on-Movies-Reviews

This is a NLP project. Basically, this is a sentiment analysis problem, in this case, consisting of a classification problem, where the possible output labels are: `positive` and `negative`. Which indicates, if the review of a movie speaks positively or negatively.
Jupyter Notebook
2
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8

autolabeling

Jupyter Notebook
1
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9

Custom_train

Python
1
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10

vision_computer_utils

Python
1
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11

E_Commerce-Data-Pipeline-for-EDA

Data pipeline for an E-commerce site in Latam to analyze company data and to understand better their performance during the years 2016-2018. We focussed on two main areas Revenue and Delivery and completed an ELT(Extraction, Load, Transformation) and EDA (Exploratory Data Analysis) using SQLite, SQL, Pandas, Matplotlib, and Seaborn.
Jupyter Notebook
1
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12

fregate_NanoServer

Shell
1
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13

SegResNet_GBMsegmentation

SegResNet for image segmentation using 11 chanels of MRI (structural and functional DSC and DTI images). Using Monai an Pytorch.
Jupyter Notebook
1
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