Quantitative Research
Quantitative Research is a structured way of collecting and analysing data obtained from different sources and can involve the use of mathematical, statistical or computational tools to derive results.
Machine Learning
Due to the empirical nature of quantitative research, methods of analysis can vary massively. Machine learning (ML) is the systematic use of algorithms and statistical models to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. ML approaches are essentially quantitative methods and models of analysing qualitative data systematically. ML algorithms work best with large data sets, when they have thousands, or millions, of sources in which to identify patterns. Machine learning methods drawn from diverse areas such as neural networks, reinforcement learning, deep learning, non-convex optimisation, Bayesian non-parametrics, NLP and approximate inference.
Deep Learning
Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. Deep Learning (DL) on the other hand, structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own. Examples of Deep Learning models are Reinforcement, Convolutional, Recurrent and Generative Adversarial Networks.
Choosing the right infrastructure
Whether you're intending on running manageable data-sets on a workstation or multiple petabytes in your data centre, choosing the right infrastructure and cooling is paramount. Partnering with Nvidia, Intel and Fujitsu, among others, we provide award wining infrastructure optimised for Quantitative Research, Machine & Deep LearningThere are no products matching the selection.