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API4AI is cloud-native computer vision & AI platform for startups, enterprises and individual developers. This repository contains sample mini apps that utilizes Furniture and Household Stuff Recognition API provided by API4AI.
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api4ai / Examples / Object Detection
MIT LicenseAPI4AI is cloud-native computer vision & AI platform for startups, enterprises and individual developers. This repository contains sample mini apps that utilizes Object Detection API provided by API4AI.
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api4ai / Examples / Image Classification
MIT LicenseAPI4AI is cloud-native computer vision & AI platform for startups, enterprises and individual developers. This repository contains sample mini apps that utilizes General Image Classification / Labelling API provided by API4AI.
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api4ai / Examples / Fashion Apparel Recognition
MIT LicenseAPI4AI is cloud-native computer vision & AI platform for startups, enterprises and individual developers. This repository contains sample mini apps that utilizes Fashion Apparel Recognition API provided by API4AI.
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api4ai / Examples / Brand Detection
MIT LicenseAPI4AI is cloud-native computer vision & AI platform for startups, enterprises and individual developers. This repository contains sample mini apps that utilizes Brand Recognition API provided by API4AI.
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Štěpán Zapadlo / Bachelor Thesis
MIT License[SCI MUNI] Data-Driven Dynamical Systems - Bachelor Thesis
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A project focused on weather classification using advanced deep learning techniques, specifically leveraging TensorFlow and a custom Convolutional Neural Network (CNN). The project involved the integration of four diverse weather datasets, namely ACDC, MWD, UAVid, and Syndrone, covering various weather conditions, including clear sky, cloudy, rainy, and sunny weather. Developed a custom CNN architecture using TensorFlow's Keras API, incorporating convolutional layers for feature extraction and dense layers for classification.
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GeoHarmonizer_INEA / eumap
MIT LicenseEumap is a library to enable easier access to several spatial layers prepared for Continental Europe, as well the source code used to produce them (http://eumap.readthedocs.org).
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Armand Rousselot / quantumML
MIT LicenseA project that introduces a Quantum Invertible Neural Network (QINN). The invertible architecture can be trained as a density estimator to perform data generation. Implemented using Pennylane.
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A little more about me... Graduated in Bachelor of Information Systems, in college I had contact with different technologies. Along the way, I took the Artificial Intelligence course, where I had my first contact with machine learning and Python. From this it became my passion to learn about this area. Today I work with machine learning and deep learning developing communication software. Along the way, I created a blog where I create some posts about subjects that I am studying and share them to help other users.
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Evaluation of various Machine learning models for sentiment analysis You are given the reviews dataset. These are 194439 amazon reviews for cell phones and accessories taken from https://jmcauley.ucsd.edu/data/amazon/ Use the “reviewText” and “overall” fields from this file. The goal is to predict the rating given the review by modeling it as a multi-class classification problem. • Take the first 70% dataset for train, next 10% for validation/development, and remaining 20% for test. • Traditional machine learning methods • Design some good linguistic features. You can start with basic TFIDF features. Use these classifiers: J48 decision trees, SVMs with linear/RBF kernel, logistic regression, xgboost, random forests and report accuracy on test set.
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Model was used in a company race to get 6th of 22 racers. Model trained for 3 hours.
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[Python] The project focuses on parallelizing the sequential implementation of K nearest neighbors (kNN) algorithm - a basic supervised learning algorithm in Machine Learning, together with evaluating its performance based on some benchmark metrics.
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Open Risk / pythondatascience
Creative Commons Attribution Share Alike 4.0 InternationalPython Data Science is an open source project providing guidance on python (and selectively R, Julia) packages relevant for data science tasks
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