Machine Learning vs Deep Learning

Artificial intelligence has been a buzzword in the IT/Data industries for the last few years. Research and developments mean that it’s rapidly moving away from concept to reality. The fields of machine learning and deep learning are contributing significantly to making artificial intelligence a tool that can benefit businesses and organisations.

But which is better machine learning or deep learning? The processes are actually both part of the same area of research – computer science – with one evolving from the other in a bid to advance artificial intelligence further.

What is machine learning?

Machine learning give computers the ability to learn without being explicitly programmed. Instead, the computer will learn by repeating processes with data, allowing it to identify hidden insights. With the rise of big data across organisations, machine learning is becoming increasingly popular.

Examples of machine learning include:

  • Sites, such as Amazon and Netflix, making recommendations based on past use
  • Credit card and loan companies using a computer to predict if an applicant is credit worthy
  • Search engines and social media platforms selecting relevant adverts to show

Machine learning is able to quickly adapt and apply its knowledge to different processes. It can support business objectives to recognise patterns in huge amounts of data and use its insights to make informed predictions based on historical information.

What is deep learning?

Deep learning has developed from machine learning. It attempts to take machine learning to the next stage by emulating the many layers of the human brain when processing information. Every time new data is added the capabilities of a computer using deep learning improves.

Examples of deep learning include:

  • Automatic machine translations, including translating content on images through identifying letters
  • Recognising images and being able to classify these to group numerous images together
  • Automatically generating captions for images by identifying the key features and notable points

Deep learning is still developing and one of the drawbacks is the huge amount of data it needs to learn from. However, it does hold huge potential for businesses in the future, such as learning to help computer languages sound natural on customer service chatbots or making improvements to speech recognition software.

Both machine learning and deep learning are pioneering the way forward for the next stage of artificial intelligence, where a machine is superior to a human brain across a range of fields, including communication skills and creativity.

Discover more about the practical examples of using Machine learning and Deep learning at Big Data LDN on 15-16 November 2017 at Olympia London, registration is free.