The goal of the AI is to provide an intelligent agent. This can also be regarded as the production of the best possible (autonomous, learning, intelligent, automatic) information system.
In the training phase, the neural network learns based on the given learning material. The learning rule specifies how the weights of the connections are modified during further learning steps. Even with high-dimensional systems and with tasks with imperfect data, such a trained network will provide robust results.
link|that uncovers an new range of possibilities with Neural Networks. But first, let's start with clarifying the terms Artificial Intelligence, Machine Learning, Neural Networks, and Deep Learning.
Artificial Intelligence (AI) and Neural Networks (NN)All technologies used in connection to the goal to create intelligent services, which were previously reserved for humans, are unified under the generic term artificial intelligence - abbreviated AI. If someone does not want to go into too much detail, he/she simply talks about AI today. AI deals with transferring individual human abilities to machines, such as the recognition of texts, image content, language and so on. For years, rapid progress has been made in these fields. 'Machine Learning', 'Deep Learning' and 'Neural Networks' are accordingly subareas of AI, partially even subareas within subareas:
Machine LearningMachine Learning is a strategy that uses training algorithms that can learn from collected data and make decisions. Instead of using a single instruction, these algorithms collect data from multiple sources to learn and make predictions. The power of these solutions in the real world is in part limited by the fact that they are based on a set of predefined algorithms more or less reminiscent of a flowchart. On the other hand, another kind of solutions does not have these limitations and therefore makes the realization of previously seemingly impossible strategies possible: Neural Networks.
Neural Networks & Deep LearningNeural Networks are an important part of Machine Learning. It is the field that will be responsible for fundamental technological upheavals in the years to come. A neural network does not have to be explicitly programmed for its tasks; it can learn from training scenarios, for example, or through reinforcement, so to speak through the 'carrot and stick' method (reinforcement learning). This is based on layered neurons and directed, weighted connections between these neurons. The following figure shows the architecture of an exemplary neural network. The task here is the recognition of numbers. The design of the proposed solution consists of three layers of neurons and their weighted connections.
Use cases at link|thatAI and Neural Networks are a corner stone of link|that's portfolio. The ability to develop agents capable of learning complex tasks that exceed human analytical potential opens up a new level of quality for our solutions. See for yourself and try the demo of link|that CARPARK - our intelligent number plate recognition software that delivers highly reliable results via reinforcement learning.
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