Your Guide to Machine Learning

Your Guide to Machine Learning

Machine Learning

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As technology evolves, terms such as artificial intelligence, machine learning and deep learning quickly penetrate public discourse. This guide will help clarify possible confusion around the topic, in order to understand the actual use of deep learning technology today.

Artificial Intelligence (AI) is essentially when machines overtake tasks which previously necessitated human intelligence. It encompasses both machine learning and deep learning.

Machine learning is where machines can learn by experience and acquire skills without human involvement.

Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similarly to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. Neural networks have various (deep) layers that enable learning, hence the term ‘deep learning’.

The staggering amount of data we generate each day makes deep learning possible.  Deep-learning algorithms require a ton of data to learn from, and the continuous increase we see in data creation is one reason that deep learning capabilities have grown in recent years.

Deep learning additionally benefits from the proliferation of AI as a Service, which has given smaller organizations access to artificial intelligence technology and specifically the AI algorithms required for deep learning without a large initial investment.

Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected. The more deep learning algorithms learn, the better they perform, as reported in

The following 8 practical examples will help further understand actual applications of deep learning:

  1. Virtual assistants – Alexa, Siri and Cortana are prime examples. Online service providers use deep learning to help understand your speech and the language humans use when they interact with them.
  2. Translations – deep learning algorithms can automatically translate between languages.
  3. Vision for driverless delivery trucks, drones and autonomous cars – deep learning enables autonomous vehicles to understand the realities of the road and react accordingly, for example, knowing a stop sign covered with snow is still a stop sign.
  4. Chatbots and service bots – those provide customer service for a lot of companies, and respond in an intelligent and helpful way to an increasing amount of auditory and text questions.
  5. Image colorization – transforming black and white images into color, which was used to be done by the meticulous human hand. Today, deep learning algorithms are able to identify the context and objects in the picture and color them accordingly.
  6. Facial recognition – not only for security purposes, but for tagging people on Facebook posts and possibly even paying for items in a store just by using our faces, in the future.
  7. Medicine and pharmaceuticals – from disease and tumor diagnoses to personalized medicines created specifically for an individual’s genome.
  8. Personalized shopping and entertainment – this is how Netflix comes up with suggestions on what you should watch next, and where Amazon comes up with the ideas for what you should buy next.