In deep learning, a neural network is a mathematical model that mimics the nervous system of the human brain and is used to learn and process complex data. It consists of multiple neurons (also known as nodes or units) that are connected to transmit and process information. Each neuron receives input from other neurons, weights and sums the input through an activation function, and then passes the output to the next layer of neurons or the final output layer.
Artificial neuron is the basic unit in neural network, simulating the function of biological neuron. It receives input signals from other neurons and processes these inputs in a weighted summation. The weighted sum is then non-linearly transformed by an activation function to produce the output of the neuron. This output will be passed to the next layer of neurons in the network.
The general steps to build and train a neural network model are as follows:
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Define the network architecture: determine the number of layers of the network, the number of neurons in each layer, and the connection method. Choose an appropriate activation function, loss function, and optimization algorithm.
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Initialization parameters: Initialize the weights and biases in the neural network, usually using random initialization.
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Forward propagation: Pass the input data through each layer of the network, calculate the output value of each neuron, and pass it to the next layer.
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Computing loss: Comparing the output of the network with the true value, a loss function is computed to measure the difference between the predicted value and the true value.
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Backpropagation: Computes the gradient of the loss function with respect to each parameter (weights and biases) in the network via the chain rule. Gradients represent how much a parameter affects the loss function.
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Parameter update: Use an optimization algorithm (such as gradient descent) to update the parameters in the network according to the direction of the gradient, so that the loss function gradually decreases.
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Repeat steps 3 to 6: Do multiple iterations until a predetermined stopping condition is reached, such as reaching the maximum number of iterations or the loss function converges.
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Prediction and Evaluation: Use the trained model to make predictions and evaluate the model's performance metrics such as accuracy, precision, and recall.
In actual implementation, deep learning frameworks (such as TensorFlow, PyTorch, Keras, etc.) can be used to conveniently build and train neural network models. These frameworks provide advanced APIs and tools that simplify the process of building and training neural network models, and provide optimization algorithms and other utility functions to help you develop deep learning models more efficiently.
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