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Introduction to classification using machine learning and deep learning (PyTorch, TensorFlow, Keras)
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Utopia / dantro
GNU General Public License v3.0 or laterdantro is a python package for handling, transforming, and visualizing hierarchically organized data.
Integrated into data-intensive projects, it supplies an easy way to define a customizable, configuration-based data processing pipeline. See utopya for an example.
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Various examples of some data analysis exercises.
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Este repositorio contiene los dos casos de estudio realizados como parte del Diplomado de Extensión en Data Science. En ambos casos, se utilizaron técnicas de programación en Python y Data Science para analizar conjuntos de datos y extraer información valiosa.
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CNU Teaching / Numerical Analysis and Practice
BSD 2-Clause "Simplified" LicenseLecture note of Numerical Analysis and Practice
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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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Prediction of age from X-Ray images of hand bones using deep learning models. We used 3 models: Shallow, ResNet50, and InceptionV4. The best result achieved was with a mean absolute error of 10 months using InceptionV4. The preprocessing of data included computer vision techniques like CLAHE filter and reducing channels, and also creativities such as using the Google MediaPipe library to detect hands and crop on them.
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This project I created to show how to develop a docker image to use for data science applications
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This Car Prediction Project aims to predict v price, using Regression models. The project contains a collection of data files, model files, and Python scripts necessary for training and deploying car prediction Price models. This project encompasses a comprehensive set of data files, serialized models, and Python scripts necessary for training, evaluating, and deploying car price prediction models.
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Program monitors selected region of the screen and detects blue quadrangles.
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Library for parsing and storing University of Wyoming sounding into HDF5 files. Original code developed by Philip Austin.
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