Xcipi

Intelligent Data

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Below is an extract from previous blog ‘Do Data Scientists Dream of Elecronic Sheep’, at the end of it I said I would revisit the topics within it further. So it is put before you again reworked. I have repeated sections of it (because I can) but mainly because the application I was working when I first wrote it is … →

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Or what do user architects have to do with machine learning? User architects are the users of the system. Even before the system exists, they enter our minds in a vague and blurry sort of way. We imagine these characters that will be the users of the system. Our imaginary and fictional users are then formed into … →

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Machine Learning Some bones Machine learning is a loose term, it covers many activities. From a software engineering aspect it could be seen as an activity evolved from pattern recognition. It can be even more narrowly viewed as the assignment of a label to a given input value. A system designed to learn … →

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10 Deep Learning Terms Explained in Simple English

Introduction

Deep Learning is a new area of Machine Learning research that has been gaining significant media interest owing to the role it is playing in artificial intelligence applications like image recognition, self-driving cars and most recently the AlphaGo vs. Lee Sedol matches. Recently, Deep Learning techniques have become popular in solving traditional Natural Language Processing problems like Sentiment Analysis.

For those of you that are new to the topic of Deep Learning, we have put together a list of ten common terms and concepts explained in simple English, which will hopefully make them a bit easier to understand. We’ve done the same in the past for Machine Learning and NLP terms, which you might also find interesting.

Perceptron

In the human brain, a neuron is a cell that processes and transmits information. A perceptron can be considered as a super-simplified version of a biological neuron.

A perceptron will take several inputs and weigh them up to produce a single output. Each input is weighted according to its importance in the output decision.

Artificial Neural Networks

Artificial Neural Networks (ANN) are models influenced by biological neural networks such as the central nervous systems of living creatures and most distinctly, the brain.

ANN’s are processing devices, such as algorithms or physical hardware, and are loosely modeled on the cerebral cortex of mammals, albeit on a considerably smaller scale.

Let’s call them a simplified computational model of the human brain.

Backpropagation

A neural network learns by training, using an algorithm called backpropagation. To train a neural network it is first given an input which produces an output. The first step is to teach the neural network what the correct, or ideal, output should have been for that input. The ANN can then take this ideal output and begin adapting the weights to yield an enhanced, more precise output (based on how much they contributed to the overall prediction) the next time it receives a similar input.

This process is repeated many many times until the margin of error between the input and the ideal output is considered acceptable.

Convolutional Neural Networks

A convolutional neural network (CNN) can be considered as a neural network that utilizes numerous identical replicas of the same neuron. The benefit of this is that it enables a network to learn a neuron once and use it in numerous places, simplifying the model learning process and thus reducing error. This has made CNNs particularly useful in the area of object recognition and image tagging.

CNNs learn more and more abstract representations of the input with each convolution. In the case of object recognition, a CNN might start with raw pixel data, then learn highly discriminative features such as edges, followed by basic shapes, complex shapes, patterns and textures.

source: http://stats.stackexchange.com/questions/146413

Recurrent Neural Network

Recurrent Neural Networks (RNN) make use of sequential information. Unlike traditional neural networks, where it is assumed that all inputs and outputs are independent of one another, RNNs are reliant on preceding computations and what has previously been calculated. RNNs can be conceptualized as a neural network unrolled over time. Where you would have different layers in a regular neural network, you apply the same layer to the input at each timestep in an RNN, using the output, i.e. the state of the previous timestep as input. Connections between entities in a RNN form a directed cycle, creating a sort of internal memory, that helps the model leverage long chains of dependencies.

Recursive Neural Network

A Recursive Neural Network is a generalization of a Recurrent Neural Network and is generated by applying a fixed and consistent set of weights repetitively, or recursively, over the structure. Recursive Neural Networks take the form of a tree, while Recurrent is a chain. Recursive Neural Nets have been utilized in Natural Language Processing for tasks such as Sentiment Analysis.

source:  http://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf

Supervised Neural Network

For a supervised neural network to produce an ideal output, it must have been previously given this output. It is ‘trained’ on a pre-defined dataset and based on this dataset, can produce  accurate outputs depending on the input it has received. You could therefore say that it has been supervised in its learning, having for example been given both the question and the ideal answer.

Unsupervised Neural Network

This involves providing a programme or machine with an unlabeled data set that it has not been previously trained for, with the goal of automatically discovering patterns and trends through clustering.

Gradient Descent

Gradient Descent is an algorithm used to find the local minimum of a function. By initially guessing the solution and using the function gradient at that point, we guide the solution in the negative direction of the gradient and repeat this technique until the algorithm eventually converges at the point where the gradient is zero - local minimum. We essentially descend the error surface until we arrive at a valley.

Word Embedding

Similar to the way a painting might be a representation of a person, a word embedding is a representation of a word, using real-valued numbers. Word embeddings can be trained and used to derive similarities between both other words, and other relations. They are an arrangement of numbers representing the semantic and syntactic information of words in a format that computers can understand.

Word vectors created through this process manifest interesting characteristics that almost look and sound like magic at first. For instance, if we subtract the vector of Man from the vector of King, the result will be almost equal to the vector resulting from subtracting Woman from Queen. Even more surprisingly, the result of subtracting Run from Running almost equates to that of Seeing minus See. These examples show that the model has not only learnt the meaning and the semantics of these words, but also the syntax and the grammar to some degree.

So there you have it - some pretty technical deep learning terms explained in simple english. We hope this helps you get your head around some of the tricky terms you might come across as you begin to explore deep learning.

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Get your money on - 5 sure fire predictions for 2016

Get your money on – 5 sure fire predictions for 2016

The first prediction is that data and analytics will continue to grow at an astounding pace and with increased velocity

This is no big surprise as all the past reports have pointed towards this growth and expansion –

Venturebeat * note that “Although the big data market will be nearly $50B by 2019 according to analysts, what’s most exciting is that the disruptive power of machine data analytics…

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Data: the new Coal and Steel

Data: the new Coal and Steel

“The Pulse of Silicon Valley,” host Joe Kernan posed the question, “What is the next really big thing?” to Ann Winblad, the legendary investor and senior partner at Hummer-Winblad. Her response: “Data is the new oil.”

There have been many who doubt what sharing data can bring. Aside from other aspects of its’ business Amazon is an online retailer, in fact this is where they…

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Three reasons why companies are not engaging with data as much as the ought to.

Three reasons why companies are not engaging with data as much as the ought to.

While a lot of tosh is talked about Big Data this not does mean it is wise to disregard the conversation. According to Hewlett Packard

87% Of enterprises cannot fully exploit their data

85% Cannot Analyse data quickly enough

92% Do not capitalise on live data 1

The appetite for data as well as the need for it are well understood. 88% of decision makers found it important, so why are around 9 out…

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Data, Continuous Improvement and Agility

Data, Continuous Improvement and Agility

“If you don’t know why you failed, how can you improve? If you don’t know why you succeeded, it must be an accident.” Lord Coe

Tomasz Tungutz, a partner at venture capitalist Redpoint Ventures (and one of my favourite writers of data-driven blog posts about key questions facing startups including how to fund raise, startup benchmarks, management best practices and team building) points out the…

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The 10 most common data issues facing business and how to cure them.

The 10 most common data issues facing business and how to cure them.

Many companies work with large amounts of data and find it leads to an increase in work loads and reduced value as the amount of data gets bigger. As the legacy data mounts up it also becomes very difficult to work with it, back it up and gain insight from it. The way companies manage data is often used as a measure of the management efficiency and maturity of that company. The problem is that as…

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Data centric alliances forged from a simple API

Data centric alliances forged from a simple API

The following use case shows how an exchange of clean blended data can simplify the implementation of commercial and strategic alliances between businesses with a common interest.

USE CASE

The company is a Professional Sports Club. They operate their own sports stadium which is well equipped with facilities for hospitality and has an integral retail outlet, refreshment and catering. Recently…

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You show me yours

You show me yours

Sharing

The last phase of the data lifecycle, sharing, is not unsurprisingly the one where the benefits of working with data are most apparent. I have discussed on a number of occasions the benefits of sharing data between colleagues and within the company and how marketing, sales, product design, operational planning and other activities are critical to decision making within a business. This…

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Data analytics made as easy as that

Data analytics made as easy as that

Some application designers must have believed that the more bits and bobs a program has the more useful it is, there are many applications that have acres of menus, scores of buttons, hundreds of areas, many diverse screens and a multitude of options; we all know, and probably use, such software. I am sure we can all remember the software we enjoyed using years ago and now wish it had not…

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Childs play

Now we have come to a stage of knowing what our future users want to use our application for, yes we asked them, we have moved onto the stage of deciding how to give them a way of using the application; in other words the interface and how the application appears on screen, be that desktop, laptop, mobile or tablet. Now it is not really possible to ask them directly how it should appear and how…

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Silos of sand

When looking at the technology needs of any business the most prevalent single problem that they all suffer from is caused by having silos of information. All of the major business consultancies, we have worked with and alongside, also recognise this as a critical problem, some even use it as a measure of the maturity of the processes that the business has in place. There are many reasons…

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Data Soup 2 Clive Longbottom Analyst at Quocirca finds that "Now, more and more binary large objects (BLOBs) are appearing in the databases – and these require different approaches to be able to identify and report on what the content actually is and in identifying patterns and making sense out of what this means to the end user." and he nails the problem on the head pointing out that "Even worse is the fact that less information is making it into standard databases – yes, there’s still an increasing amount of numerical and textual data being created that resides within a database, but this is being outstripped by the amount of information that is being created in a more ad hoc manner with files that lie directly in a filing system." 

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Data can help you know who your audience is.

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Data Soup