Deep Learning versus Machine Learning – both are methods of how artificial systems learn. Find out what the learning methods are all about and where the actual differences lie here.
Deep Learning vs. Machine Learning
Before we show you the concrete differences between Deep and Machine Learning, here is a definition of both methods.
- Machine learning or machine learning is based on mathematical methods that are supposed to recognise a pattern, which is then generalised. In this process, an artificial system acquires knowledge from experience.
- Pattern recognition in machine learning works, for example, via hierarchy formation in decision trees, which compares a variable with a specified value and then classifies it as true or false, for example. Equally possible is the creation of vector similarities, where the nearest neighbour of a value K counts, which a system must find.
- Most machine learning methods are supervised, i.e., the human gives clear instructions and classes to the system and it must output the appropriate values. Machine learning algorithms are used in many ways in everyday life, e.g. when a smartwatch analyses your movement profile.
- The use of machine learning becomes difficult if too little data has been entered or if the data is too multi-dimensional. Since the previously determined data is of great importance for a machine learning system, it only works if a suitable amount of data is available.
- Machine learning also extends into Deep Learning to some extent. Here, however, artificial neural networks are used that are modelled on biological networks such as the human brain.
- Deep learning gets its name from the fact that it works with deep dimensions that would cause problems for machine learning. Here, the system learns on its own and does not receive any data in advance.
- Deep learning, as used for example in artificial intelligences, also needs errors, as a system thereby learns which information was correct and needs to be strengthened in the neural network.
- For Deep Learning in particular, the help of humans is always needed, for example, to provide information to an AI. For example, Google’s “AlphaGo” programme managed to beat a human at the board game “Go” in 2015.
What are the differences between Deep Learning and Machine Learning
Deep learning has similarities with machine learning, but the two learning methods differ significantly in some ways.
- Deep learning draws on methods from machine learning, but not vice versa.
- While for machine learning multiple dimensions are too complex for a system, deep learning builds on just that depth.
- Since machine learning is often supervised, the decisions of such learning systems are more transparent than those of deep learning. Here, an artificial neural network is at work, which, similar to a biological one, also makes non-traceable decisions.
- Deep learning is also very important for AI research, but not yet as elaborate as machine learning.