Computing systems inspired by the structure and functioning of the human brain.
More about Neural Network:
Neural Networks are a set of algorithms designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling, and clustering of raw input. These algorithms are modeled after the human brain, with layers of interconnected nodes, akin to neurons.
Neural networks can learn and make independent decisions by analyzing data. They are used in a variety of applications that involve pattern recognition such as image and voice recognition, medical diagnosis, and financial forecasting.
Frequently Asked Questions
How do Neural Networks "learn"?
Neural Networks "learn" by adjusting weights and biases in response to the input data they receive. This process is iterative and requires large datasets and computational power. As data passes through the network, it fine-tunes its weights to reduce the difference between the predicted output and the actual target values.
What are the layers in a Neural Network?
A Neural Network typically consists of an input layer, one or more hidden layers, and an output layer. Each layer contains a number of nodes or "neurons". Information flows through these layers, getting processed at each stage, and results in an output prediction.
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