Info about AI and GDPR
Difference Between Deep Learning and Machine Learning
It may be useful to know the difference between deep learning and machine learning when developing an AI model.
Here you can read about the difference between deep learning and machine learning
In short, the difference between deep learning and machine learning is that deep learning structures algorithms in different layers, so that the mathematical model can learn and make decisions entirely on its own. Machine learning instead receives a lot of data that it interprets through algorithms, learns from and makes its own decisions based on the information that is based on its knowledge. Note that deep learning is a form of machine learning.
Machine learning
When you train an algorithm with data that results in a mathematical model, it constitutes machine learning. The mathematical model is also called an AI model. Through the data that the algorithm has been trained with, the AI model can arrive at a reasonable answer based on statistics when it receives a question.
In other words, machine learning is a process whereby the computer develops the ability to create knowledge and adapt to a specific task, without the computer having been programmed in advance for the specific task in question.
From data to AI model: Machine learning
To train an AI model through machine learning, it must gain experience. This is done by feeding information into the AI model. The more information that is entered, the better the AI model tends to be, as it gains more experience. If the data contains personal data, it is important to keep in mind that the GDPR applies to the processing. The AI model can draw conclusions about unknown data even though it has not been included in the training.
Common steps in machine learning
Companies that want to develop an AI model through machine learning usually do so through the following three steps:
1. Collect a large amount of data, such as the mass of one's own texts and/or other selected sources (for example government authorities), to train the AI model.
2. Identify different structures and recurring patterns when training the AI model.
3. The AI model is created and can thus process new data and draw conclusions about new issues based on the data with which it has been trained.
Practical example of machine learning
1. A company that wants to create an AI model selects a large number of images of objects in space.
2. The AI model then identifies different structures and patterns that correspond to how objects move in space.
3. Adjustments are made to distinguish between different types of objects in space, such as the sun, moon, stars and planets.
4. The AI model that is created can then be used to identify what an object in space constitutes.
Deep learning
Deep learning is a form of machine learning. The AI model is built in a way that resembles the neuronal network of the human brain.
Deep learning is a form of machine learning
To create an AI model so that it resembles the human brain’s neuron network, the artificial neuron network should consist of three parts:
Input
XXX
Hidden layers
It may be a hidden layer but is not uncommon with several. The more data the model has used in training, the more hidden layers.
Output
XXX
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Training AI models to produce the best results possible
It is important to train an AI model in order for it to generate the best possible result. The more data it gets, the better the results should be. However, it is not the only factor that matters. It is important that the data is qualitative as well. In other words, relevant to the purpose of the AI model. Whether it’s regular machine learning or deep learning, there are different training methods that can be used.