โ ๐๐๐ซ๐๐๐ฉ๐ญ๐ซ๐จ๐ง: The Neuron of the Digital Brain ๐ง
Perceptron is considered the building block of Deep Learning, the simplest Neuron available. Itโs an algorithm that is used for Supervised machine learning.
๐๐ฒ๐ฉ๐๐ฌ ๐จ๐ ๐๐๐ซ๐๐๐ฉ๐ญ๐ซ๐จ๐ง๐ฌ:
(1) Single Perceptron
(2) Multi-layer Perceptron (MLP)
โจ ๐๐ซ๐๐ก๐ข๐ญ๐๐๐ญ๐ฎ๐ซ๐ ๐จ๐ ๐๐ข๐ง๐ ๐ฅ๐ ๐๐๐ซ๐๐๐ฉ๐ญ๐ซ๐จ๐ง โจ
๐๐๐ข๐ ๐ก๐ญ๐ฌ ๐๐ง๐ ๐๐ข๐๐ฌ: Normally, w1 and w2 represent the weights, while b is the bias term. We typically use the formula ( xw1 + yw2 + b ) in linear classification. We determine whether the result is positive or negative by substituting these values into the equation.
๐๐ฎ๐ฆ: The summation is the next component in a perceptron. Illustrated in the middle of the diagram, it aggregates all the values to produce a net result, which could be either positive or negative.
๐๐๐ญ๐ข๐ฏ๐๐ญ๐ข๐จ๐ง ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง: The final component of a perceptron is the activation function. This can be any function that provides a specified range of values.
๐๐ข๐ฆ๐ข๐ญ๐๐ญ๐ข๐จ๐ง: It will not work properly for non-linear data.
โจ ๐๐ซ๐๐ก๐ข๐ญ๐๐๐ญ๐ฎ๐ซ๐ ๐จ๐ ๐๐ฎ๐ฅ๐ญ๐ข-๐ฅ๐๐ฒ๐๐ซ ๐๐๐ซ๐๐๐ฉ๐ญ๐ซ๐จ๐ง โจ
A multi-layer perceptron has been introduced to overcome the limitation of a single perceptron, Which is mainly used in Artificial Neural Networks. By combining multiple โsingle perceptronโ we can create a multi-layer perceptron It consists of at least three layers of nodes: an input layer, one or more hidden layers, and an output layer.
๐๐ง๐ฉ๐ฎ๐ญ ๐๐๐ฒ๐๐ซ: This is the first layer that receives the input features of the data.
๐๐ข๐๐๐๐ง ๐๐๐ฒ๐๐ซ๐ฌ: These are intermediate layers between the input and output layers. An MLP can have one or more hidden layers, each layer containing multiple neurons. The role of the hidden layers is to capture complex patterns and relationships in the data.
๐๐ฎ๐ญ๐ฉ๐ฎ๐ญ ๐๐๐ฒ๐๐ซ: This is the final layer that generates the output predictions.
๐๐ข๐ฆ๐ข๐ญ๐๐ญ๐ข๐จ๐ง: Training of the MLP, especially with many hidden layers and neurons, can be computationally intensive. Also, it requires a large amount of data.
#๐๐๐ญ๐๐๐ง๐๐ฅ๐ฒ๐ฌ๐ข๐ฌ #DataScience #๐๐๐ญ๐๐๐ง๐ ๐ข๐ง๐๐๐ซ๐ข๐ง๐ #๐๐๐๐ก๐ข๐ง๐๐๐๐๐ซ๐ง๐ข๐ง๐ #๐๐๐๐ฉ๐๐๐๐ซ๐ง๐ข๐ง๐ #๐๐๐ฎ๐ซ๐๐ฅ๐๐๐ญ๐ฐ๐จ๐ซ๐ค #๐๐ข๐ ๐ข๐ญ๐๐ฅ๐๐ซ๐๐ข๐ง #๐๐ซ๐ญ๐ข๐๐ข๐๐ข๐๐ฅ๐๐ง๐ญ๐๐ฅ๐ฅ๐ข๐ ๐๐ง๐๐ #๐๐๐ง๐๐ซ๐๐ญ๐ข๐ฏ๐๐๐ #๐๐๐ซ๐๐๐ฉ๐ญ๐ซ๐จ๐ง #๐๐๐
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