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{{see also|Machine learning terms}}
{{see also|Machine learning terms}}
==Introduction==
==Introduction==
Layers are a fundamental building block in artificial neural networks, machine learning algorithms modeled after the structure and function of the human brain. They perform computation on input data to produce outputs which can be used for making predictions or solving other problems. The number of layers within a neural network as well as its size and configuration determine its capacity and expressive power.
[[Layer]]s are a fundamental building block in [[artificial neural network]]s, the [[machine learning]] [[algorithm]]s [[model]]ed after the structure and function of the human brain. Each layer performs specific computations on it; its output serves as input for subsequent layers. Neural networks consist of multiple interconnected layers that work together to process input data and make predictions.


==Types of Layers in Neural Networks==
==Types of Layers in Neural Networks==
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- Fully Connected Layers: Layers in which every neuron is connected to every neuron in the previous layer.
- Fully Connected Layers: Layers in which every neuron is connected to every neuron in the previous layer.
==How Layers Work in Neural Networks==
Each layer in a neural network performs computations on data received from the previous layer, using weights and biases. These operations can be described mathematically as an activation function which maps inputs to outputs. These weights and biases are learned by the network through training, where its parameters are updated in order to minimize loss functions which measure differences between predicted outputs and actual ones.
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.
==Introduction==
Machine learning algorithms employ neural networks, which are inspired by the structure and function of the human brain. Neural networks consist of multiple interconnected layers that work together to process input data and make predictions. Each layer performs specific computations on it; its output serves as input for subsequent layers.
One of the foundational concepts in neural networks is "layer". In this article, we'll give a detailed explanation of what a layer is and how it's utilized in machine learning applications.
==What is a Layer?==
In a neural network, each layer is composed of artificial neurons that perform specific computations on input data. The input to one layer is made up of activations from the previous one, and its output serves as input for the next one. Each neuron in this layer is connected to all neurons in its predecessor and produces an output consisting of weighted sums of these inputs followed by application of an activation function.
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.
==Types of Layers==
Neuronal networks employ various layers, each with its own special properties and capabilities. Some of the most frequently employed layers include:


===Dense Layers===
===Dense Layers===
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===Recurrent Layers===
===Recurrent Layers===
Recurrent layers are algorithms that apply the same set of weights to input data at each time step, while also taking into account information from previous steps. They're commonly employed in sequence modeling tasks such as natural language processing or speech recognition.
Recurrent layers are algorithms that apply the same set of weights to input data at each time step, while also taking into account information from previous steps. They're commonly employed in sequence modeling tasks such as natural language processing or speech recognition.
==How Layers Work in Neural Networks==
Each layer in a neural network performs computations on data received from the previous layer, using weights and biases. These operations can be described mathematically as an activation function which maps inputs to outputs. These weights and biases are learned by the network through training, where its parameters are updated in order to minimize loss functions which measure differences between predicted outputs and actual ones.
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.
==What is a Layer?==
In a neural network, each layer is composed of artificial neurons that perform specific computations on input data. The input to one layer is made up of activations from the previous one, and its output serves as input for the next one. Each neuron in this layer is connected to all neurons in its predecessor and produces an output consisting of weighted sums of these inputs followed by application of an activation function.
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==
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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)==
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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.


==Explain Like I'm 5 (ELI5)==
==Explain Like I'm 5 (ELI5)==
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[[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]]
[[Category:Terms]] [[Category:Machine learning terms]]
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