Introduction to Deep Learning

Over the past few years, the term “deep learning” has taken over the internet and firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics.

What is Deep Learning ?
Deep Learning refers to artificial neural networks that are composed of many layers. It is the new big trend in Machine Learning. It promises general, powerful and fast machine learning, moving one step closer to AI.

What is a Neuron ?
The basic cell in a biological brain is called a neuron (type of nerve cell). There are 100 billion neurons in a human brain. These neurons are connected to form a biological neural network with each network having an average of 100,000 connections.

In computer science neuron (artificial neuron) is a mathematical function conceived as a model of biological neurons, a neural network. It receives one or more inputs and sum them to produce an output (activation).
In simple: a neuron takes an input, applies some mathematical transformation to it and gives an output.

So if this is a single neuron, neural network, really a tiny little neural network, a larger neural network is then formed by taking many of the single neurons and stacking them together. So, if you think of this neuron that’s being like a single Lego brick, you then get a bigger neural network by stacking together many of these Lego bricks

The figure above is the basic neural network with four input feature. The input features might be the size, number of bedrooms, the zip code or postal code, and the wealth of the neighborhood. And so given these input features, the job of the neural network will be to predict the price y. And notice also that each of these circle, these are called hidden units in the neural network, that each of them takes its inputs all four input features. So for example, rather than saying these first nodes represent family size and family size depends only on the features X1 and X2.
Instead, we’re going to say, well neural network, you decide whatever you want this known to be. And we’ll give you all four of the features to complete whatever you want. So we say that layers that this is input layer and this layer in the middle of the neural network are densely connected. Because every input feature is connected to every one of these circles in the middle. And the remarkable thing about neural networks is that, given enough data about x and y, given enough training examples with both x and y, neural networks are remarkably good at figuring out functions that accurately map from x to y.