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What is Deep Learning?

Deep Learning is a method from information processing that analyzes large amounts of data using neural networks. This approach is largely modeled on the biological processes in the human brain, with the difference that processing such data sets would be nearly impossible for our brain.

How does Deep Learning work?

Deep Learning includes algorithms that are programmed to learn without human intervention. The technical basis of these programs are neural networks. These consist of many layers of neurons, just like our brain. In the input layer, all the information arrives that is to be processed. In our biological example, this would be the sensory impressions from eyes, fingers, etc. At the end of the network, one or more responses are resulting in the output layer, depending on the inputs. For example, if we see a lion in the immediate vicinity, our reaction is to quickly get to safety.

In order for this appropriate response to occur, we must process the inputs correctly. This happens in the layers between the input and output layers, the so-called hidden layers. Based on past experience, stronger or weaker connections form between neurons from different layers. The more intermediate layers a network has, the “deeper” it is. This is where the term “deep” learning comes from.

This example can be transferred almost one-to-one to the technical algorithm. We define a neural network with a certain number of layers and neurons. In most cases, more neurons can be used to learn more complex facts. So the more complex the use case, the larger the neural network. With the help of training data, the model then learns to link the correct neurons with each other, so that the correct relationship between model input and output is created. From the outside, we only specify what the correct prediction should look like. The model learns to make the right connections within the network on its own.

Practical applications for Deep Learning

Today, we already encounter neural networks unconsciously in everyday life. They attempt to learn correlations from past data that can be applied in future situations.

  • Dynamic Pricing: This is about setting specific prices for the same products depending on the customer, country or other circumstances. A few years ago, this was mainly limited to airlines, which adjusted flight prices accordingly the closer the departure date came. Today, this strategy is conceivable in many areas, for example in e-commerce, where customers are offered particularly favorable bundles to lure them back into the store.
  • Product Recommendation: This is another use case that is primarily used in e-commerce and aims to suggest a suitable product to the customer based, for example, on their purchase history, search behavior or other customer characteristics. In addition, such algorithms are also used by Netflix or Amazon Prime to suggest a suitable series or movie.
  • Fraud Detection: This is the automated detection of conspicuous behavior of all kinds, which usually indicate misuse of the system. The most famous use case is bank accounts on which conspicuous debits or credit card transactions take place, which could indicate that the credit card has fallen into the wrong hands.

Deep Learning vs. Machine Learning

Deep Learning is a subfield of Machine Learning that differs from Machine Learning in that no human is involved in the learning process. This is based on the fact that only Deep Learning algorithms are able to process unstructured data, such as images, videos or audio files. Other machine learning models, on the other hand, need the help of humans to process this data, telling them, for example, that there is a car in the image. Deep Learning algorithms, on the other hand, can automatically convert unstructured data into numerical values and then incorporate these into their predictions and recognize structures without any human interaction having taken place.

In addition, deep learning algorithms are able to process significantly larger amounts of data and thus also tackle more complex tasks than conventional machine learning models. However, this comes at the expense of a significantly longer training time for deep learning models. At the same time, these models are also very difficult to interpret. That is, we cannot understand how a neural network arrived at a good prediction.

This is what you should take with you

  • Deep Learning is a subarea of Machine Learning and describes a method of information processing.
  • Neural networks in particular are used to exploit correlations from large data sets, which can then be applied in future situations.
  • Deep Learning is already used today, for example, in product recommendations in e-commerce or in fraud detection in the banking sector.
  • Deep Learning differs from Machine Learning primarily in that it can also handle unstructured data such as images, videos or audio recordings.

Other Articles on the Topic of Deep Learning

  • IBM has an exciting article describing other Deep Learning applications.
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