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As the name suggests, neural networks are inspired by the brain.
A neural connection is designed to mimic how our brains work to recognize complex patterns and improve over time.
Their abilities make neural networks an essential tool for many industries.
In medicine, they help in diagnosing diseases, and medical imaging.
Neural networks have also become an essential tool in autonomous vehicles.
These nodes, also called neurons, are the basic units that process information.
Interconnected like a web, these neurons work together to do the computations in the neural data pipe.
Each node in this layer represents a specific feature of the data.
Hidden Layers:Nestled between the input and the output layers are a series of intermediate layers.
It is these layers that help the web connection identify complex patterns by unraveling the data step by step.
The number of hidden layers in a connection depends on the complexity of the task.
How do Neural Networks work?
Every node in the neural web connection goes through a lot of steps.
First, it gets data from the input layer or the previous hidden layer.
One node is assigned to one feature or attribute of the data.
Next, each input is multiplied by a weight.
These weights are values that help determine the importance of the input.
To begin with, these weights are assigned randomly.
Together with the weights, all inputs also get a bias value.
Think of this as an additional tunable parameter that helps make the connection more efficient and accurate.
This process continues until all the hidden layers have been traversed.
Types of Neural Networks
Neural networks come in all shapes and sizes.
Broadly speaking they all follow the same process described above, but cater to different kinds of use cases.
Feed-forward Neural Networks:These are the simplest of the lot.
These networks dont employ any feedback loops or cycles, and the data is processed sequentially without any iteration.
This layer applies filters to the input and scans the data for identify patterns.
These networks are useful in situations where context is essential to predicting an outcome.
RNNs are commonly used in speech recognition andnatural language processing.
They include a generator model and a discriminator model that together help generate new synthetic data.
