Mastering Neural Networks: The Essential Guide to Single Perceptrons 🤖

Understanding the fundamentals of neural networks is very important. For this reason, understanding how a single perception works is a good place to start.

A single perception can separate two linearly separable classes in 2D or 3D. This is done but using the perception learning algorithm, which is also called the perception learning rule, or just the delta rule.

When a given x1 and x1 input is given the perception can predict whether the output is going to be class 1 or class 2. If there is an error the weights and bias can be adjusted in a direction that will reduce the error. This is done over multiple epochs until the network is trained.

An example of a perceptron separating two classes of data is provided in 2D and 3D. This is done using the step activation function. An in-depth discussion about how the boundary layer gets adjusted is provided. We then look at how a single perceptron can solve some logic gate cases such as the AND, OR NAND, and NOR Gates. However, a multi-layer neural network will be needed to solve the XOR and XNOR cases.

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