Glossary

Neural Networks

Neural Networks are a type of artificial intelligence modeled after the structure and functioning of the human brain. Using a complex system of algorithms and mathematical models, these networks are designed to recognize patterns and relationships in data, and make predictions based on that information.

At their core, neural networks are composed of interconnected nodes, organized into layers. Each node receives input from multiple other nodes, and processes that input using a set of mathematical functions. The output of each node is then passed on to other nodes in the next layer, until the final output is produced.

There are many different types of neural networks, each with its own set of strengths and weaknesses. Some are designed for image recognition, while others are better suited for natural language processing or predictive analytics.

Despite their complexity, neural networks are becoming increasingly common in a wide range of applications, from self-driving cars to medical diagnosis. With the ability to recognize and learn from patterns in large amounts of data, they offer a powerful tool for businesses and researchers alike.

Overall, neural networks are a fascinating and rapidly evolving field of artificial intelligence, with the potential to revolutionize the way we interact with technology and process information.

A wide array of use-cases

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