Neural Network in Machine Learning, Artificial Intelligence, and Deep Learning
Introduction
Technology is becoming more embedded in our daily lives by the minute, and in order to keep up with the pace of consumer expectations, companies are more heavily relying on learning algorithms to make things easier. You can see its application in social media (through object recognition in photos) or in talking directly to devices (like Alexa or Siri).
These technologies are commonly associated with artificial intelligence, machine learning, deep learning, and neural networks, and while they do all play a role, these terms tend to be used interchangeably in conversation, leading to some confusion around the nuances between them. Hopefully, we can use this blog post to clarify some of the ambiguity here.
Artificial Intelligence, Machine Learning, Neural Networks, and Deep learning. How They Works?
Deep Learning is a branch of Machine Learning (ML) that uses Deep Neural Networks to solve problems in the ML domain. Perhaps the easiest way to think about artificial intelligence, machine learning, neural networks, and deep learning is to think of them like Russian nesting dolls. Each is essentially a component of the prior term.
Neural Network in Machine Learning, Artificial Intelligence, and Deep Learning Architectures
The “deep” in deep learning is referring to the depth of layers in a neural network. A neural network that consists of more than three layers—which would be inclusive of the inputs and the output—can be considered a deep learning algorithm.
Neural networks are algorithms that are loosely
modeled on the way brains work. These are of great interest right now because they can learn how to recognize patterns. A famous example involves a neural network algorithm that learn to recognize whether an image has a cat, or doesn't have a cat.
The architecture above is commonly referred to as multi Layer Perceptron (MLP) or Fully Connected Layer. This architecture has 3 neurons on the Input Layer and 3 nodes on the Output Layer. Between Input and Output, there are 2 Hidden Layers with each having 4 neurons.
Conclusion
While all these areas of AI can help streamline areas of your business and improve your customer experience, achieving AI goals can be challenging because you'll first need to ensure that you have the right systems in place to manage your data for the construction of learning algorithms. Data management is arguably harder than building the actual models that you'll use for your business.