Neural Networks are a subset of Machine Learning techniques which learn the data and patterns in a different way utilizing Neurons and Hidden layers. Neural Networks are way more powerful due to their complex structure and can be used in applications where traditional Machine Learning algorithms just cannot suffice.
By the end of this tutorial, you will have the knowledge of:
- A brief history of Neural Networks
- What are Neural Networks
- Types of Neural Networks
- Perceptron
- Feed Forward Networks
- Multi-Layer Perceptron
- Radial Based Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Long Short-Term Memory Networks
A Brief History of Neural Networks
Researchers from the 60s have been researching and formulating ways to imitate the functioning of human neurons and how the brain works. Although it is extremely complex to decode, a similar structure was proposed which could be extremely efficient in learning hidden patterns in Data.
For most of the 20th century, Neural Networks were considered incompetent. They were complex and their performance was poor. Also, they required a lot of computing power which was not available at that time. However, when the team of Sir Geoffrey Hinton, also dubbed as “The Father of Deep Learning”, published the research paper on Backpropagation, tables turned completely. Neural Networks could now achieve which was not thought of.
What are Neural Networks?
Neural Networks use the architecture of human neurons which have multiple inputs, a processing unit, and single/multiple outputs. There are weights associated with each connection of neurons. By adjusting these weights, a neural network arrives at an equation which is used for predicting outputs on new unseen data. This process is done by backpropagation and updating of the weights.
Types of Neural Networks
Different types of neural networks are used for different data and applications. The different architectures of neural networks are specifically designed to work on those particular types of data or domain. Let’s start from the most basic ones and go towards more complex ones.
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