Data engineering is a rapidly growing field that focuses on designing, building, and maintaining the data architecture and infrastructure required for organizations to effectively manage and analyze their data.
ASR is a challenging task in natural language, as it consists of a series of subtasks such as speech segmentation, acoustic modelling, and language modelling to form a prediction (of sequences of labels) from noisy, unsegmented input data. Deep learning has replaced traditional statistical methods...
Data ScienceData fusionComputational SciencesMachine LearningScientific ComputingData-driven modellingModellingData Science is today one of the main buzzwords, be it in business, industrial or academic settings. Machine learning, experimental design, data-driven modelling are all, undoubtedly, rising......
“deep” and “shallow”. The complex, nonlinear DNN is capable of learning rich representations of relationships in the data and generalizing to similar items via embeddings, but needs to see many examples of these relationships in order to do so well....
DAMA International, originally founded as the Data Management Association International, is a not-for-profit organization dedicated to advancing data and information management. Its Data Management Body of Knowledge, DAMA-DMBOK 2, covers data architecture, governance and ethics, data modelling and design...
A data hub is a high capacity, high-throughput integration point – such as anApache Kafkamessaging system, that can be used for monitoring, inspecting, routing, and acting upon data in motion. The idea is that all the evented data feeds that the organisation has are hooked up to the data...
The computational predictive modeling approach differs from the mathematical approach because it relies on models that are not easy to explain in equation form and often require simulation techniques to create a prediction. This approach is often called “black box” predictive modeling because the mod...
This is still the Achilles heel of current computational drug-discovery efforts, and hence also poses problems when applying AI. We are able to describe chemistry rather well, and have a large amount of proxy assay data available for modelling, hence this type of data has been a key focus ...
Architectural technology is a vast discipline in itself. At present, innovations like Building Information Modelling (BIM), computational design, robotic fabrication, building performance analysis, and artificial intelligence dominate the discipline. Several topics of research are underway, exploring a futu...
Neural networks are sophisticated techniques capable of modelling extremely complex relationships. They’re popular because they’re powerful and flexible. The power comes in their ability to handle nonlinear relationships in data, which is increasingly common as we collect more data. They are often us...