Deep Learning / sciences

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Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

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Intro to Machine Learning and Deep Learning for Earth-Life Sciences

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Example code from the book "Deep Learning for the Life Sciences"

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Website for the Deep Learning for Physical Sciences workshop at NIPS 2017

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This repository contains NLU related material for the I833 Deep Learning course at University of Applied Sciences Dresden

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Student material for the applied deep learning course "Neural Networks and Deep Learning for Life Sciences and Health Applications"

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This is the material for the lecture on Convolutional Neural Networks at the course "Deep Learning" 2020 at university of applied sciences Dresden.

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Repository for class project "Application of 3D graphic synthetic dataset generation for the means of image alpha matting using deep neural networks" of Machine Learning class, Faculty of Applied Sciences of Ukrainian Catholic University (Lviv)

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STNeuroNet 🌿
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Software for the paper "Fast and robust active neuron segmentation in two-photon calcium imaging using spatio-temporal deep learning," Proceedings of the National Academy of Sciences (PNAS), 2019.

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Recent advances in machine learning have brought forth methods that have been remarkably successful in a variety of settings, most notably in image analysis. These methods are now being applied to data analysis in marine sciences, where they have the potential to automate analysis that previously required manual curation. Here we adapt a machine learning model intended for object recognition to the task of estimating age from otolith images. The model is trained and validated on a collection of otolith images from Greenland halibut. We show that the precision of the model's age estimates is comparable to and may even surpass that of human experts. Age reading from otoliths is an important element in the management of many marine stocks, and automating this analysis is an important step to ensure consistency, lower cost, and increase scale.

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