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Why are Neural Networks needed in Machine Learning?
Neural networks have been around for many years, through which they have been praised as well as criticised for their characteristics.
But off late, they have gained attention over other machine learning algorithms. Of course, Machine learning algorithms are important as they help achieve certain goals. But what should we do when machine learning algorithms can’t achieve higher accuracy?
This is where deep learning algorithms come into play. They mimic the layers of the human brain, and try to take optimal decisions by passing an input from one layer to the next.
Neural networks, as the name suggests, tries to follow the pattern of decision-making taken by the human brain.
We haven’t achieved the level of intelligence that the human brain uses until now, but observing the latest technologies and the improvements, it isn’t a far day when neural networks would perform as well, if not better, as the human brain.
They have been used in reinforcement learning with the help of Q-learning to achieve high accuracy.
The attention gained can be credited to the computers being fast, efficiently being able to use large data sets, usage of graphical processing units (GPU), better algorithms, and the architecture of neural network.
As an example, we can see the ‘ImageNet’ database.
It contains more than a million high resolution coloured images from more than thousand categories.
The categories range from cars to animals to trees.
One of the tasks using this ‘ImageNet’ database is to classify unknown images present in these categories to a specific category of images.
Convolutional neural networks have shown high performance with improved percentage of accuracy.
But deep learning algorithms have been used by tech-giants like Google and Apple for the purpose of speech recognition and translation respectively.
Before jumping into these advanced concepts, let us understand a neural network that has one or two layers.
These neural nets with one or two layers are not of much use, but are helpful in understanding the inner working of the neural network.
Knowing the theory of neural networks, how they work, and the significance of layers helps lay a foundation deep learning. Understanding how simple networks work will lay a foundation that will help us to easily understand how deep networks work.
The first, and the most basic example of a neural network that we will learn is a ‘perceptron’.
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