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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
In [[machine learning]], a [[layer]] is a set of [[neurons]] ([[node]]s). [[Neural network]]s consist of multiple interconnected layers that work together to process [[input]] | In [[machine learning]], a [[layer]] is a set of [[neurons]] ([[node]]s). [[Neural network]]s consist of multiple interconnected layers that work together to process [[input]] data and make predictions. Each [[layer]] performs specific computations on the input data, and its output serves as input for the subsequent layer. | ||
[[Neuron]]s -> [[Layer]]s -> [[Neural Network]]s | |||
*[[Neuron]]s form [[layer]]s which form [[neural network]]s. | *[[Neuron]]s form [[layer]]s which form [[neural network]]s. | ||
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*[[Input layer]]: The initial layer in a neural network that takes input data and passes it along to the next one. | *[[Input layer]]: The initial layer in a neural network that takes input data and passes it along to the next one. | ||
*[[Hidden layer]]: | *[[Hidden layer]]: Sublayer(s) situated between the input and output layers that perform intermediate computations on input data. | ||
*[[Output layer]]: The final layer in a neural network that generates the final output based on the intermediate computations performed by the hidden layers. | *[[Output layer]]: The final layer in a neural network that generates the final output based on the intermediate computations performed by the hidden layers. | ||
*[[Convolutional layer]]: Used in convolutional neural | *[[Convolutional layer]]: Used in [[convolutional neural network]]s ([[CNN]]s), these layers perform [[convolution]] operations on input data to [[extract features]] and [[reduce spatial dimensions]]. | ||
*[[Recurrent layer]]: Recurrent neural | *[[Recurrent layer]]: [[Recurrent neural network]]s ([[RNN]]s) employ layers with a [[memory]] mechanism that enables them to process [[sequence]]s of data and recognize [[temporal dependencies]]. | ||
*[[Dense layer]] ([[Fully connected layer]]): Layers in which every neuron is connected to every neuron in the previous layer. | *[[Dense layer]] ([[Fully connected layer]]): Layers in which every neuron is connected to every neuron in the previous layer. | ||
*[[Pooling layer]]: reduces the spatial dimensions of the input while retaining important information in the data. It applies an aggregation function, such as max or average pooling, to small rectangular regions of the input data, resulting in an output with fewer dimensions. Commonly used in [[CNN]]. | *[[Pooling layer]]: reduces the [[spatial dimensions]] of the input while retaining important information in the data. It applies an aggregation function, such as [[max pooling|max]] or [[average pooling]], to small rectangular regions of the input data, resulting in an output with fewer dimensions. Commonly used in [[CNN]]. | ||
==How Layers Work in Neural Networks== | ==How Layers Work in Neural Networks== | ||
Each layer in a neural network performs computations on data received from the previous layer. A layer's computation can be represented as the dot product of inputs and weights, followed by application of an activation function. The outputs from a layer are then fed back into the next one in the network, with this cycle repeated until an accurate final output is produced. These weights and biases are learned by the network through training, where its parameters are updated in order to minimize loss functions | Each layer in a neural network performs computations on data received from the previous layer. A layer's computation can be represented as the [[dot product]] of inputs and weights, followed by the application of an [[activation function]]. The outputs from a layer are then fed back into the next one in the network, with this cycle repeated until an accurate final output is produced. These [[weights]] and [[biases]] are learned by the network through training, where its parameters are updated in order to minimize loss functions that measure differences between predicted outputs and actual ones ([[labels]]). | ||
==How Layers are Structured in Neural Network== | ==How Layers are Structured in Neural Network== | ||
[[Input layer]] -> [[Hidden layer]](s) -> [[Output layer]] | |||
Neural networks consist of three layers: input layers, hidden layers and output. The input layer is the initial hub in the network and it receives input data before passing it along to the next. Conversely, the output layer produces the final output after processing all previous input data. Finally, hidden layers sit between input and output layers and perform most of the computation for a network. | Neural networks consist of three layers: input layers, hidden layers and output. The input layer is the initial hub in the network and it receives input data before passing it along to the next. Conversely, the output layer produces the final output after processing all previous input data. Finally, hidden layers sit between input and output layers and perform most of the computation for a network. | ||
==Training a Neural Network== | ==Training a Neural Network== | ||
Training a neural network involves altering the weights and biases of neurons in each layer to minimize error between predicted output of the network and actual output. This is typically accomplished using backpropagation, which propagates error from one layer back through all others in turn and adjusts weights | Training a neural network involves altering the weights and biases of neurons in each layer to minimize [[error]] between predicted output of the network and actual output. This is typically accomplished using [[backpropagation]], which propagates error from one layer back through all others in turn and adjusts weights and biases accordingly. | ||
During the training process, weights and biases in each layer are adjusted using an optimization algorithm such as stochastic gradient descent that minimizes errors. The optimization iteratively updates weights and biases until errors have been eliminated from each layer. | During the training process, weights and biases in each layer are adjusted using an [[optimization algorithm]] such as [[stochastic gradient descent]] that minimizes errors. The optimization iteratively updates weights and biases until errors have been eliminated from each layer. | ||
==Explain Like I'm 5 (ELI5)== | ==Explain Like I'm 5 (ELI5)== | ||
Picture yourself standing atop an enormous tower made up of building blocks. Each block can be seen as a layer in your machine learning model. | |||
The bottom block serves as the | The bottom block serves as the input layer, providing the machine with all of the basic information it needs to make a decision. From there, each block on top adds more insight until finally, the very top block (output layer) makes its final judgment. | ||
Just as building a tower with many blocks makes it stronger, having multiple layers in a machine learning model enhances its capacity for understanding and making decisions. | Just as building a tower with many blocks makes it stronger, having multiple layers in a machine learning model enhances its capacity for understanding and making decisions. | ||