Buzz words like neural networks, logistic regression, machine learning and deep learning are popping up more and more. Deep learning has been the focus of active research that aims to evaluate its function and strives towards illuminating how its methods are impacting traditional machine learning approaches.
What is Deep Learning?
The short answer: Deep learning is defined as a sub set of artificial intelligence that uses computer algorithms to create autonomous learning from data and information. Computers that inhibit machine learning functions are able to change and improve algorithms freely. The focus of current research lies within the development of computer programs adapting at the exposure to new data.
Deep Learning: Core Concepts
There are a few terms and concepts you should be familiar with when it comes to deep learning.
Logistic Regression – The regression analysis identifies a connection between input variable to predict outcome variables. It is a simple classification algorithm for learning to make decisions between predicting different variables. Let’s take handwriting as an example: whether it is an I or L, etc.
Activation Function – Nonlinear activation functions are applied to layers and allow Neural Networks to identify complex decision boundaries.
Artificial Neural Network – Input data is taken in, transformed and applied. The repetition of steps allows the artificial neural network to learn several layers of non-linear features and ultimately creates a prediction as the final layer (output). It learns by generating an error signal measuring the differences between predictions.
Layer – Deep learning consists of building blocks, and layers are the highest level building blocks bordered by input, and output layers are the hidden layers. The received weighed input is transformed and then passed on as output to the next layer.
Artificial Neuron or Unit – A unit refers to the activation function and inhibits numerous incoming and outcoming connections. More complex units are referred to as long short term memory units.
Why Deep Learning Is Relevant
Deep learning surrounds us every day, and this will only increase with time. Whether you are are thinking about cars that drive autonomously or even have some new technology like parking assistance, traffic control, or face recognition technology at airports. Reports on events are likewise written by computers, more precisely: Computers using deep learning technologies to remain a constant learning process.
Machine learning enables the communication between computers and humans. This includes our ability to communicate with cars, identify terrorist suspects or report on subject matters. Not only will their function increase over time, it will impact most industries as well as the jobs that come with them. Therefore, it is helpful for employers and managers likewise to learn about its development, how it is evolving and where it is heading. Eventually it will impact all of us – possibly on a daily basis.
Why Deep Learning is Important for Companies
Corporate learning is so important! The era of digitalization has allowed the technology to flourish, and computers that are able to analyze massive amounts of complex data known as ‘big data’ can now provide more accurate results. This helps companies increase their chance to identify profitable opportunities or avoid unknown risks.