deep learning
Projects with this topic
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Python application, built on top of Keras, that helps doctors classify breast tumors as benign or malignant, by using ANN architecture and Logistic + Softmax regression. Conducted a small study on how different predictive models can deliver different performances in breast tumor prediction through Deep Learning.
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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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FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer
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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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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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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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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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Predicting pathogenic potentials of short DNA reads with reverse-complement deep neural networks.
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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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A Machine Learning approaches to identify genetic variants associated with disease
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Neural Network software for identifying atoms and atomic columns in High Resolution Transmission Electron Micrographs (HRTEM)
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這個專案展示在人工智慧相關技術方面的能力,包括數據預處理、視覺化、特徵選擇、模型訓練以及使用卷積神經網絡(CNNs)、循環神經網絡(RNNs)、長短期記憶網絡(LSTMs)和生成對抗網絡(GANs)等技術。
This project showcases our capabilities in AI-related technologies, including data preprocessing, visualization, feature selection, model training, and the use of advanced techniques such as CNNs, RNNs, LSTMs, and GANs.
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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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Private git for rendering ".ipynb" notebook files...
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