Introduction to Self Organising Maps

Introduction to Self Organising Maps

Self Organising Maps or SOM for short were originally invented by a Finnish professor, Teuvo Kohonen in the 1980s and therefore are sometimes called a Kohonen map. They are a form of neural network but are unusual in that they do not require labelled data to train (and are therefore…

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Bayes Theorem – Getting a better understanding of Probability

All Probabilities Are Conditional Even when statisticians tell you that the probability of some event is A what they are actually saying is that given all of the background the probability is A. Something as simple as rolling a six sided die gives you a 1/6th probability of any individual…

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The Truth About Building A Deep Learning Model

In a previous post I talked about my project to use a Convolutional Neural Network (CNN) as a pre parser for security logs. I recently got a dump of anonymised data from a friend which arrived in directories identifying what it was and with two additional headers per file with…

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Getting started with Machine Learning

So the cool kids keep talking about machine learning and how it is changing everything and you want to be part of that conversation? No problem, here is a roadmap to get you started… First of all, learn python programming. In my opinion everyone should know a little programming but…

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Understanding Neural Networks – Part Three

Training Neural Networks We talked previously about how an artificial neuron (from now on lets call them Perceptrons like the cool kids do) generated its output from its inputs using its activation function. We also mentioned briefly that the inputs were altered by the weights on the input synapses. Now…

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Understanding Neural Networks – Part Two

In Part 1, we introduced the idea of a neuron as the building block for neural networks, it has inputs and outputs and uses an activation function to generate the output from the weighted inputs. This time we are going to explore the activation function in a bit more detail…

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Understanding Neural Networks – Part One

Part One in a series... Welcome to part one in a series helping demystify Neural Networks. The aim of this series is to give you a solid understanding of neural networks and Deep Learning (DL) so that you can start to develop your skills to actually build simple DL models…

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Build your own ML workstation

Machine learning tends to require quite considerable hardware due to the large number of calculations required. Whilst it is easier than ever to rent your hardware from the major service providers like Amazon and google and from specialist Machine Learning cloud providers like Databricks, there is a certain satisfaction in…

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Build your own Deep Learning UEBA system?

At the time of writing this article I lead the Solutions Architecture team at Exabeam, a UEBA based SIEM company. As such, I tended to get into some pretty interesting conversations with customers about both security monitoring and data science. My favourite conversation by subject is definitely when customers tell…

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Welcome to the home of advanced Information Security. Here you can learn about using Machine Learning and advanced analytics to improve your security environment.

In addition we will provide impartial advice about security technologies such as SIEM (Security Information and Event Management) and UEBA (User and Entity Behavioral Analysis) systems.

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