Deep Learning

The more we unlock, the more accuracy we gain for future projects.

We use deep learning to complement our intelligence modules. The ability to interrogate across platforms, networks and layers within repositories allows the learning intelligence that supports the Quantum AI engines to be accurate and available with relevant current and historical data.
This means for users the ability to research across, down and into where the information can be retrieved from makes a good user experience with accurate results that’s consistently learning from the last, otherwise referred to as Quotient referencing, one quantity within another.

Neural Flexibility

Deep learning is embodied in our broader family of machine learning methods based on artificial neural networks: these methods all contribute to the Quantum AI engine, embracing supervised, semi-supervised or unsupervised learning. This brings flexibility to projects depending on the challenge and the requirements. Quantum AI Engine enables businesses to stay in front and find the edge over competitors.

Deep learning can tackle any big data

Challenging the engine

Humans become distracted and tired and sometimes make careless mistakes, especially with the mundane processes. But when it comes to neural networks, this isn’t the case. Once trained, a deep learning model becomes able to perform thousands of routine, repetitive tasks within a relatively short time. Automation we design is to help people in their lives to make informed decisions, an application to assist and enhance jobs in industry, not to replace them.

In many organisations, data is unstructured: much of it remains in different formats such as images, text, and so on and across any number of silos. This can prove problematic for machine learning algorithms to mine this data, and it’s where Deep Learning engines come into their own.

Large unstructured data is no match for deep learning

You could be facing multiple challenges with big data or complex problems, but Deep Learning can help

Routine Task Assistance

Unstructured data
Different formats, different places, unrecognisable.
Simple ML algorithms cannot mine this data.

Routine Task Assistance

Careless mistakes
Mundane tasks are tedious, and humans are
more likely to make mistakes.

Routine Task Assistance

Repetitive tasks
Your workforce waste time performing the same tasks over and over again.
You need to implement some form of automation.

Routine Task Assistance

No analytical development
You need analytics that learn from experience,
otherwise referred to as Quotient referencing, one quantity within another.

For years, coders have been programming computers so that they perform repetitive tasks for us. Now they automate our repetitive thoughts.

– Clive Thompson, Author

How Deep Learning Can Help

Firstly the training phase can be considered as a process of labelling large quantities of data and identifying their matching characteristics. The Quantum System compares those characteristics to derive accurate results when it encounters similar data the next occasion. During the next stage, inferring, the model makes conclusions assisted by the knowledge it gained previously.

During the training of deep learning models, large volumes of data combined with neural network architecture learn from the data directly and automatically without the need for feature extraction or manual processes.

Creating the competitive edge with deep learning

Diving Into the Deep End

To perform Deep Learning it is essential to access data repositories to provide the depth of research and analysis to advance the user experience, accurate results and continual learning measures for a successful AI project. On its own DL can feed into other queries too as a result of the Quantum AI engine, such as SIEM queries and look up as well as cross uses into SASE and applications in Development, HR, Accounting and Finance to reference just a few.

You can use different data formats to train deep learning algorithms and still obtain insights which are relevant to the purpose of the training.

DL is like a human left loose in a library; the more we see and use, the more we learn and gain experience, knowledge and accuracy.

– Steve Watts, Founder of Quantum AI

Improved Performance
and Data Mining

Deep learning helps to disentangle different abstracts and select which features improve performance. Improvements mean that accuracy and depth of learning is enhanced, and thus Quantum AI can provide the user with greater depth that’s improving its results; providing the recipient of improved performing information to base decision-making upon.

Deep learning can manage complex datasets

Implementation timeline

Quantum AI