Neural Networks
A neural network is a type of machine learning algorithm that is modeled after the structure and function of the human brain. It consists of layers of interconnected "neurons," which process and transmit information.
The basic building block of a neural network is the neuron, which takes in inputs, performs a computation on them, and produces an output. The inputs are typically real-valued numbers, and the computation performed by each neuron is a simple mathematical operation such as a dot product.
The layers of neurons in a neural network are organized into an input layer, one or more hidden layers, and an output layer. The input layer receives the initial input data and the output layer produces the final predictions. The hidden layers in between the input and output layers are used to extract features and patterns from the data.
The neural network learns to make accurate predictions by adjusting the strengths of the connections between the neurons, this process is called training. The training process is done by adjusting the weights of the connections between the neurons, so the output of the neural network is as close as possible to the desired output. There are different techniques to adjust the weights, the most popular is backpropagation algorithm that uses the gradient descent optimization method. This process can be summarized as follows:
Feed the input data through the network to calculate the output.
Compare the output with the desired output and calculate the error (loss)
Use the error to calculate the gradient of the loss function with respect to each weight
Update the weights with the gradients, this is done by subtracting a small fraction of the gradient from the weight.
Repeat the process for a number of iterations or until the loss function converges to a minimum value.
By adjusting the weights in this way, the neural network is able to learn and improve its performance over time.
Neural networks have taken the world by storm in the last decade, and are used in numerous fields ranging from computer vision to forecasting.
The basic building block of a neural network is the neuron, which takes in inputs, performs a computation on them, and produces an output. The inputs are typically real-valued numbers, and the computation performed by each neuron is a simple mathematical operation such as a dot product.
The layers of neurons in a neural network are organized into an input layer, one or more hidden layers, and an output layer. The input layer receives the initial input data and the output layer produces the final predictions. The hidden layers in between the input and output layers are used to extract features and patterns from the data.
The neural network learns to make accurate predictions by adjusting the strengths of the connections between the neurons, this process is called training. The training process is done by adjusting the weights of the connections between the neurons, so the output of the neural network is as close as possible to the desired output. There are different techniques to adjust the weights, the most popular is backpropagation algorithm that uses the gradient descent optimization method. This process can be summarized as follows:
Feed the input data through the network to calculate the output.
Compare the output with the desired output and calculate the error (loss)
Use the error to calculate the gradient of the loss function with respect to each weight
Update the weights with the gradients, this is done by subtracting a small fraction of the gradient from the weight.
Repeat the process for a number of iterations or until the loss function converges to a minimum value.
By adjusting the weights in this way, the neural network is able to learn and improve its performance over time.
Neural networks have taken the world by storm in the last decade, and are used in numerous fields ranging from computer vision to forecasting.
Updated on: 30/01/2023
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