01/03/2018 12:02:00 (GMT)
Deep Learning is a subsection of Machine Learning. It mimics the way a human brain is structured and learns as a human would through being shown. This means that a Deep Learning solution is able to understand what features in the input data given are important to the output without any additional input from a human. Deep Learning is achieved through the use of Artificial Neural Networks in architectures made up of connected neurons that execute mathematical functions on the data flowing through them to produce meaningful output.
One of the main factors in the accuracy of a Deep Learning solution is the amount of data that is available to train on. This input data can be either unstructured or structured: sound, text, video, images or numerical. With sufficient data a Deep Learning solution is capable of producing output predictions and inferences comparable with, and sometimes exceeding, that of a human.
With the use of Convolutional Neural Networks and more recently Capsule Neural Networks we are able to produce Deep Learning models that can achieve extremely high levels of image classification results. To date this technology has been applied in many different ways from Disease Diagnoses to Self-Driving Cars.
Sequence data is usually thought of as numerical time series-based data. While it is true that Deep Learning models can be very accurate with forecasting of such data it should also be understood that text-based data also falls under the category of sequence data. Deep Learning is a great tool for understanding sequences such as sales or inventory in a business but can also be applied where you may not have previously considered. For example, customer reviews are largely in the form of unstructured text-based data. Having a system that can automatically read and tag reviews with sentiment as well as categorise them would lead to a quicker understanding of customer needs. This also has the benefit of freeing up staff for tasks that require more of a human element and so increasing the efficiency of your team.
Our team of skilled researchers and software developers are able to help your business enter the world of Machine Learning and Deep Learning by helping you realise the ideas that you may have thought impossible to achieve with your data. Below are just some specific areas that we could assist with.
Hyper personalised marketing is what is expected by most consumers today and most likely by all consumers in the future. Using Deep Learning to help with marketing not only achieves this bespoke tailoring to the consumer but can also help increase conversation rates. Potentially this could mean strategies that include delivery of marketing emails during time slots that a user is most likely to respond positively; or a sales team being able to identify the most likely convertible leads through predictive scoring.
There has been a lot of hype over recent years surrounding self-driving cars, with the most accurate and cost-effective solutions being provided through Deep Learning systems. However, Deep Learning has more to offer the automotive industry than just automated driving systems. Safety in cars that still require a human operator is one such area, where detecting driver drowsiness and triggering alerts can be realised through the use of Deep Learning techniques.
We live in a world where surveillance is everywhere whether we like it or not. With all of the different ways in which we can be monitored it is not surprising that the amount of visual and audio data captured daily is significant enough for Deep Learning to be applied successfully. Using the data generated in this way could lead to systems that can recognise the same faces from photographs and captured video. Furthermore, examples where councils have managed to improve traffic warden efficiency can be found where number plates and even vehicle types have been identified through Neural Network software.
Healthcare stands to benefit a huge amount through the application of Deep Learning models. There is so much data in this sector that if brought together and understood by machines could lead to huge leaps forward in standards of care available to patients. One area of impact includes assisting diagnosticians through Deep Learning solutions that can understand MRI scans and recognise visual markers for diseases like cancer. Another is the prediction of risk factors for individual patients as well as monitoring of patients in real time to spot early patterns of serious diseases developing. Increased efficiency in hospitals and GP Surgeries can also be achieved by applying Neural Networks to problems. For example, suggesting personalised timelines for conducting scans to check for returning diseases in patients; or identifying the likelihood of a patient to be readmitted with the same issue within a six-month period.
The biggest threat to the manufacturing process is inefficiencies. One such inefficiency is that of down time. This can occur when machinery breaks down and requires maintenance. Deep Learning can help reduce the impact of such events on the manufacturing process through something called Predictive Maintenance. The machinery used by your organisation can be monitored by a Neural Network that can forecast if it is likely to fail in the near future. Allowing a business to schedule the downtime of that machine rather than it being an unwelcome surprise. This means that contingencies can be put in place before a machine stops working and also means that a full-time maintenance crew is no longer necessary and can be called in on scheduled days instead.
Finance is the area that Quanovo started in with Machine Learning where we have applied Deep Learning to understanding financial markets. Being able to use a Neural Network to predict variations in market data while processing enormous quantities of information at very fast speeds means that an army of analysts is no longer necessary. However, Deep Learning applications go beyond this and can be applied to other areas in finance as well. For example, Deep learning-powered fraud models can help accurately determine if a customer will default on a credit loan or not, thereby helping credit providers identify its likely most profitable customers ahead of time.
Much like with marketing, todays consumer expects a higher level of personalisation from customer service. Also, just like marketing this area has already produced massive amounts of historical customer service data that can be fed into a Deep Learning system to create valuable output. A good application could be an automated customer service chat bot that has benefited from the experience of decades of customer service conversation data. Another popular application of Deep Learning for customer service is that of Product or Service Recommendations that are highly personalised to the customer.
The day to day mundane tasks that any business must address is the perfect area for the application of a Deep Learning solution. Having a machine handle the monotonous and repetitive work means that you or your staff are freed up to focus on other areas of their roles that might require more of a human touch. Fortunately, those types of tasks are usually the more engaging and so by having a machine handle the other tasks productivity could actually increase across the company. Some examples of where Deep Learning could be applied include during the hiring process where a Neural Network based system can quickly process all submitted job applications and filter them down to a small realistic subset that are well suited for the role on offer. Another application that would go towards addressing the problem of inefficient meetings would be to have a Deep Learning system summarise the minutes of each meeting and send out to-do lists to all the meeting participants via email after the meeting.
If you would like to find out more about how Deep Learning can be applied to your business specifically and how we can help please contact us.