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Denis Nutiu / Image Tagger
GNU General Public License v3.0 or laterImage Tagger is an application that predicts an image's tags using deep-learning. It is useful for photographers who want to improve their workflow by auto-tagging images.
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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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Real-time Gender and Age Recognition from Audio
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ICT deep learning lab
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Tangible AI / public / alt-text-generator
GNU General Public License v3.0 or laterGenerate ALT text (captions for low vision website users or book readers).
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A trainable AI with data in text format. Deterministic
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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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Controversy quantification of topics on twitter, based on user probability to participate in a controversy topic, using GNN and NLP models.
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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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This is the unpaired image-2-image and volume-2-volume translation project. It converts images or volumes of an input domain to a target domain using artificial intelligence.
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The aim of this project is to provide an exploratory analysis of Domain Adaptation (DA) techniques in the context of PHM for Bearings fault prognosis, focusing on Health Index (HI) estimation and Remaining Useful Life (RUL) prediction. The adopted dataset is the PRONOSTIA/FEMTO-ST bearings dataset.
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Final project of course ML junior by Skillbox. Goal: Build model to predict client's loan default. Metrics: ROC-AUC
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Master thesis - Deep learning-based pairwise alignment of protein sequences using transformer architecture.
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Detector reconstruction of gamma-rays using deep learning.
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MLP and CNN to classify TinyImage30 dataset. Fine Tuned models. Applied Grad-Cam to identify parts of the image that highly impact the classification based on model convolution gradients. Feature-2-Seq RNN encoder/decoder network trained on the COCO dataset. The produced model is able to predict reasonable captions for provided test images.
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