In this article, I will explain What are Neural Networks.
Neural networks are a type of machine learning algorithm that are modeled after the structure and function of the human brain. They are composed of interconnected nodes, called neurons, that are organized into layers. The input layer receives data, which is then processed through one or more hidden layers, and finally produces an output.
Each neuron in a neural network performs a simple mathematical operation on its input and produces an output. The output of one neuron is then passed as input to the next neuron in the next layer. The weights and biases of the neurons are adjusted during training using optimization algorithms like backpropagation, to optimize the model’s predictions.
Neural networks can be used for a variety of tasks, such as image and speech recognition, natural language processing, and even playing games like chess and Go. They are particularly useful when dealing with large, complex datasets where traditional algorithms may struggle to find meaningful patterns or relationships.
Further Reading
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What is Bucket Sort Algorithm?
What is deep learning and why is it important?
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