Unraveling the Mystery of Neural Networks: How AIs Think

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Unraveling the Mystery of Neural Networks: How AIs Think

Table of Contents

  1. Introduction
  2. Neurons and Neural Networks
    1. What are Neurons?
    2. Neural Networks
  3. Perceptrons and Activation Functions
    1. Perceptrons
    2. Activation Functions
  4. Building a Neural Network
    1. Example of a Neural Network
    2. The Need for Bias Neurons
  5. Genetic Algorithms and Learning
  6. Conclusion

Neurons and Neural Networks

Neural networks are a Type of artificial intelligence that attempts to copy the brain. To understand how neural networks work, we need to start with the basics: neurons.

What are Neurons?

The brain is made up of about a hundred billion neurons, which allow us to think, make decisions, and do everything we do in our daily lives. Neurons have three main parts: dendrites, the SOMA, and the axon.

Dendrites connect neurons to other neurons and receive input from them. The soma is the middle part of the neuron, and the axon is like the output of the neuron. When a neuron receives enough positive input from other neurons, it sends a positive spike down the axon, which releases neurotransmitters to all neurons connected to its axon branches. This produces a positive spike in those neurons, and the process repeats.

The brain is made up of billions of these neurons, and some connections between neurons are stronger than others. This means that if there's a strong connection between neuron A and B, but a relatively weak connection between neuron A and C, when neuron A is triggered, neuron B receives a much larger positive spike than neuron C. This means that neuron B is a lot closer to being triggered than the other neuron.

Neural Networks

Neural networks are made up of connected neurons, and some connections between neurons are stronger than others. The strength of each connection is called the weight, and it usually ranges between negative 1 and 1. A red line represents a positive connection, and a Blue line represents a negative connection.

A single neuron in a neural network is called a perceptron. It has many connections to other perceptrons coming in and going out. Inside the circle bit, there are two processes: summing up all the connections coming into the perceptron and the activation function.

Perceptrons and Activation Functions

Perceptrons

A perceptron sums up all the connections coming into it and then passes the result through an activation function. There are many activation functions You can use, but for this article, we'll be looking at one of the simplest activation functions: the step function.

Activation Functions

The step function takes the sum of the inputs and returns 1 if the sum is positive and 0 if the sum is 0 or negative. The output of the activation function is then multiplied by the weights associated with each connection, and the process repeats.

Building a Neural Network

Example of a Neural Network

Let's look at an example of a neural network that can recognize a checkerboard pattern. The camera sees a 2 by 2 image, and pixels can only be black or white. There are two possibilities that we want: either a black and white checkerboard pattern or a white and black checkerboard pattern.

The neural network is made up of connected neurons, and each layer combines features from the previous layer. The input layer has four neurons corresponding to the four pixels from the camera. The bottom neuron in the Second layer combines the features of the top right and bottom left pixels, resulting in a diagonal line that, combined with the white diagonal line in the opposite direction, creates a checkerboard pattern.

The Need for Bias Neurons

The biased neuron is always outputting a 1 and is multiplied by a weight of -1. This means that We Are always subtracting 1 from that neuron. The biased neuron is needed because we want the neuron in the third layer to be activated only when the total is more than 1, instead of the Current situation, which activates on an input greater than zero.

Genetic Algorithms and Learning

Neural networks get more complicated than the example we just looked at, so it's usually not an option to manually set all the neurons and weights. This is where the genetic algorithm comes in to evolve and learn the weights that Create the desirable behavior. Those with better behavior will survive and pass on their genes, which in this case are the weights, and slowly through the magic of evolution, the neural network learns how to do the desired tasks.

Conclusion

Neural networks are a type of artificial intelligence that attempts to copy the brain. They are made up of connected neurons, and some connections between neurons are stronger than others. A single neuron in a neural network is called a perceptron, and it has many connections to other perceptrons coming in and going out. The biased neuron is needed because we want the neuron in the third layer to be activated only when the total is more than 1. The genetic algorithm is used to evolve and learn the weights that create the desirable behavior.

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