4 of 4 Neural Network Jobs in the South East

these areas is essential. Essential Skills: .NET C# Mvc (Framewrk and/or Core) WebApi SignalR Python programming experience Previous experience in the development and use of Convolution Neural Networks (CNN s) for machine vision and image analysis applications Experience with large-scale data sets Experience with open source machine learning platforms such as Keras/tensorflow or More ❯

these areas is essential. Essential Skills: .NET C# Mvc (Framewrk and/or Core) WebApi SignalR Python programming experience Previous experience in the development and use of Convolution Neural Networks (CNN's) for machine vision and image analysis applications Experience with large-scale data sets Experience with open source machine learning platforms such as Keras/tensorflow or More ❯

these areas is essential. Essential Skills: .NET C# Mvc (Framewrk and/or Core) WebApi SignalR Python programming experience Previous experience in the development and use of Convolution Neural Networks (CNN’s) for machine vision and image analysis applications Experience with large-scale data sets Experience with open source machine learning platforms such as Keras/tensorflow or More ❯

and deep learning. On the one hand, Bayesian learning provides a theoretically sound framework to formalise the estimation of the architecture and the parameters of deep neural network models. On the other hand, deep learning offers new tools in Bayesian modelling, e.g. to learn flexible nonparametric priors or computationally efficient posterior distribution approximations. State of the art … The field of machine learning has recently been much impacted by deep learning. Deep neural networks are now at the basis of the state-of-the-art in computer vision, natural language processing, to cite just a few. While very effective, these models are computationally costly and require large quantities of data for their many parameters to be … grounded framework to reason about uncertainty, and it is one of the cornerstones of modern machine learning. At the same time, the theory at the basis of deep neural networks is not yet very well understood and its grounds must be laid out. Although the interaction between these two learning paradigms is relatively under-explored, there is a More ❯