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" 2019 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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Keeping roads in a good condition is vital to safe driving. To monitor the degradation of road conditions is one of the important component in transportation maintenance which is labor intensive and requires domain expertise. Automatic detection of road damage is an important task in transportation maintenance for driving safety assurance. The intensity of damage and complexity of the background, makes this process a challenging task. A deep-learning based methodology for damage detection is proposed in this project after being inspired by recent success on applying Deep- learning in Computer Sciences. A dataset of 9,053 images is taken with the help of a low cost smart phone and a quantitative evaluation is conducted, which in turn demonstrates that the superior damage detection performance using deep-learning methods perform extremely well when compared with features extracted with existing hand-craft methods. Using convolutional neural networks to train the damage detection model with our dataset, we use the state-of-the-art object detection method, and compute the accuracy and runtime speed on a GPU server. At the end, we show that the type of damage can be distinguished into eight types with acceptable accuracy by applying the proposed object detection method.

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