Aimed at independent users, this course in artificial intelligence (AI) and neural networks will take you through some of the practical considerations to make when looking at how neural networks can be used in your business. From identifying numbers and images to modelling energy consumption and making financial predictions, this course will take you through practical applications of neural networks and highlight areas where you can apply natural language and image or signal processing in your industry.
Participants will learn about various types of neural networks such as convolution neural networks, recursive neural networks and generative adversarial networks while exploring their practical application areas from use cases in industry.
You will also completed Practical exercises where you will be introduced to the two main python libraries that are used with neural networks namely Pytorch and Tensorflow. You will gain some hands-on experience of applying existing libraries and pretrained neural networks to various small-scale problems.
Learning objectives
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Understand the concept of Artificial Neural Networks and Deep Neural Networks.
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Gain familiarity to different types of advanced neural networks (Deep Neural Networks, Convolution Neural Networks and Recursive Neural Networks) and areas of application for each type.
Pre-requisites
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Understanding of basic mathematical concepts e.g. functions, matrices and derivatives.
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Minimal experience with Python
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