Introduction Artificial Neural Networks (ANNs) are modeled after the biological neural networks found in our brains. These networks consist of many interconnected neurons that work together to process information, find patterns, and make predictions. The Basics At their core, ANNs are a mesh of nodes, known as neurons, that are connected through weighted inputs and outputs. This means that each neuron can receive inputs from multiple other neurons and use that information to produce an output. These outputs can then be used as inputs by other neurons, creating a complex web of interconnected neurons. The weights of these connections are adjusted during a process known as training. This is where the ANN learns to recognize patterns and make predictions based on the input it receives. The goal is to minimize error, or the difference between the expected output and the actual output. Applications ANNs have a wide range of applications, from speech recognition to image classification. They can be used to predict future trends in the stock market, diagnose cancer, or even control autonomous vehicles. One example of an ANN application is in facial recognition software. ANNs can be trained on thousands of images of faces, and then used to recognize new faces and match them with identities. This technology is in use today in security and law enforcement agencies around the world. Conclusion Artificial Neural Networks are a powerful tool for processing information and making predictions. They are modeled after the biological neural networks found in our brains, and can be used in a wide range of applications. With advancements in technology and machine learning, ANNs are becoming more powerful every day. Navegação de Post Unlocking the Potential of Smart Contracts The Future is Here: The Rise of Autonomous Vehicles
