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A neural network is a type of machine learning model inspired by the structure and function of the human brain. It is composed of interconnected nodes, called neurons, which are organized into layers. Information flows through the network in a feedforward manner, with each neuron in one layer connected to the neurons in the next layer.
At the input layer, the neural network receives input data. This could be numerical data, images, text, or any other type of data that can be represented numerically. Each input neuron receives a specific value from the input data.
The information then propagates through the hidden layers of the neural network. In these layers, the neurons apply weights to the inputs received and perform calculations using an activation function. The activation function introduces non-linearity into the model, allowing it to learn complex patterns and relationships in the data.
The output layer of the neural network produces the final prediction or classification based on the input data. Each output neuron represents a class or label that the model is trained to predict. The values produced by the output neurons are often transformed using a softmax function to represent the model’s confidence in each class.
The neural network learns by adjusting the weights and biases of its neurons through a process called backpropagation. During training, the model compares its predictions to the true labels of the training data and calculates an error. The error is then used to update the weights and biases in such a way that the model’s predictions are improved.
Once trained, a neural network can be used for various tasks, such as image classification, speech recognition, natural language processing, and more. Its ability to learn from large amounts of data and discover complex patterns makes it a powerful tool for many applications in artificial intelligence.
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