machine learning
Projects with this topic
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Developed a project utilizing Generative Adversarial Networks (GANs) to convert grayscale images to RGB color images. Leveraged deep learning techniques to train the GAN model on a dataset of grayscale and corresponding color images, achieving realistic colorization results. This project demonstrated proficiency in image-to-image translation and advanced deep learning methodologies within the realm of computer vision.
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Machine learning techniques to classify water samples as drinkable or not drinkable.
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FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer
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Lyric to musical melody generation task
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Automated pipeline for decensoring of doujinshis, using HentAI and DeepCreamPy. Supply an Imgur album link, nhentai link or nhentai id, wait 15-30 minutes, and download significantly decensored images.
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This learning project uses a neural network to predict the winner of a League of Legends game after 10 minutes ingame given the current data. The main branches notebook has an accuracy of roughly 58.5%, while the NN with manually added weights has one of roughly 66.9% but has problems with the validation accuracy and loss.
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$ mldev | is a data science experiment automation and reproducibility toolkit.
check our experiment templates: https://gitlab.com/mlrep
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This projects implements neural-network controllers for two agent-based models: (i) a predator-prey model and (ii) a metabolic network model.
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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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A 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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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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[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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The goal of the project is to build a workflow for modeling topics from youtube video transcript in the context of the Tournesol project : https://tournesol.app/
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A library of blog posts and tutorials. Part of the End-to-End Machine Learning School at e2eml.school/courses
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Collection of completed data-mining (university course) on python
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Workshop held in the Summer School on Optimization and Machine Learning at ZIB in Berlin, 2023.
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