Caffe
Caffe is a deep learning framework that works well with big data. More general details about the Framework are given in our article on Caffe Deep Learning. This article describes a Caffe model and...
Caffe is a deep learning framework that works well with big data. More general details about the Framework are given in our article on Caffe Deep Learning. This article describes a Caffe model and...
AutoKeras is an open source Framework based on Keras to enable Neural Architecture Search (NAS) for deep learning architectures trained on big data. NAS is a technique to support the human design for neural...
Snake Make is a workflow management system used for reproducible and scalable big data analysis. It is often used in bioinformatics, but can be used also in other application areas. It is a user-friendly...
💊 Precision medicine benefits are possible when medical decisions are based on individual patients. The fundamental idea of this data-driven medical approach is described in our article on precision medicine. This article lists six...
⚕️ Precision medicine is a data-driven medical approach that bases medicial decisions on invididual patients by using and individual patient characteristics. The approach is also called stratified medcine or personalized medicine. Some also call...
🔬 Biomarkers stand for any measurable quantity of big data or score that is used to stratify patients. In other words, stratifying patients mean in this context to arrange or classify them into well-defined...
🖧 Autoencoder refers to an unsupervised learning technique that is often used in the context of dimensionality reduction of big data. It projects some data from a higher dimension to a lower dimension via...
🕸️ Sequence to sequence learning with neural networks is a very effective method for predictions of sequences. Applications using sequence data are often in natural language processing (NLP) like speech recognition or machine translation....
Euler equation refers actually to a set of equations named after Leonhard Euler. These equations are used in physical sciences in the field of fluid dynamics. They are also known as Eulerian functions of...
Dimensionality reduction techniques are methods to reduce the dimensionality of a modeling problem. This is very important when working with big data and high-dimensional data sets. Learning from this data is a very challenging...