What are Neural Networks and how do they differ from Machine Learning and Deep Learning?
Have you ever wondered what Machine learning and Deep learning are? How they differ from Neural networks? What do all these terms mean, that everyone is talking about? Well, don't panic as we'll guide you through all of them with these short and yet simple explanations.
What is Machine Learning?

Machine Learning, Deep Learning, and Neural Networks are all essential components of one another, so to understand Neural Networks, we have to start with Machine Learning. If you imagine the Russian doll "Matryoshka", it's all quite similar to that. Let's say Artificial Intelligence is the biggest doll, that holds inside of her a smaller one called Machine Learning, that one holds in the Deep Learning and the smallest one is the Neural Network toy – the heart (brain) of everything.

So what is Machine Learning? Very generally speaking – Machine Learning is learning from examples. We give our machine as many examples as we can and the machine learns from those examples as long as it can classify and distinguish things correctly as we've taught it.
Machine Learning is learning from examples
Machine Learning vs Deep Learning

Deep Learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured, and interconnected. The more Deep Learning learns, the better the algorithm performs. Deep learning needs far less human intervention than machine learning.
Neural Network mimics our brain
Neural Network is a network of functions. It mimics our brain through a set of algorithms. At a basic level, a Neural Network is made of four main components: inputs, weights, a bias or threshold, and an output. Neural networks are the backbone of deep learning algorithms.
Coming into this, the first thing to understand is that NOBODY knows everything. Feel comfortable where you are and keep being curious.
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