In this series we work towards deep learning. To paint a clear picture of deep learning, however, we first discuss machine learning and neural networks. To explain deep learning clearly, it is important to outline the connections between all these concepts. In this introduction we give a brief overview of the concepts machine learning, neural networks and deep learning, and the connections between them.
Artificial Intelligence (A) is intelligence exhibited by machines. The term AI is often used when a machine mimics cognitive functions that are associated with humans. But where does this intelligence come from? It is difficult or even impossible to formulate formal rules with sufficient complexity, that accurately describe our world. It is therefore necessary to give AI systems the opportunity to acquire their own knowledge by extracting patterns from data. This skill is known as machine learning.
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Machine learning refers to any system where the performance of a machine in performing a task improves by gaining more experience in performing that task. Machine learning consists of algorithms that learn thanks to data. In machine learning, a trend line is usually calculated on the basis of data. The system can recognize a pattern in a cloud with points. The system can also use that pattern to make predictions.
Machine learning is, in short, the design of machines that can learn from data. There are different ways to design these machines. Neural networks are an important approach to machine learning. In this case, the algorithms in machine learning are implemented by using the structure of neural networks. These neural networks model the data using artificial neurons. Neural networks thus mimic the functioning of the brain, which forms a biological neural network.
As mentioned, in this series of articles we work towards deep learning. Machine learning includes deep learning. Both are the techniques by which computer systems can learn with the help of experience. Deep learning, however, is a complex form of machine learning. For example, multilayer neural networks and non-linear transformations are used. In other words, deep learning consists of algorithms that make it possible to train computers by exposing multilayer neural networks to huge amounts of data (Big Data).
Machine learning, neural networks and deep learning
In summary, machine learning is the field that deals with the design of machines that can learn from data. One of the models that can be used in machine learning is the neural network. Deep learning is a complex form of machine learning in which multiple layers of neural networks are used to design machines that can learn from data.
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