We reside in a world, where we are constantly surrounded by deep learning algorithms be it for the good or worse cause. From the Netflix recommendation system to Tesla’s autonomous cars, Deep Learning is leaving its stark appearance in our lives. This quaint & lucid technology is something that can impeach the whole thousand years old human civilization with just 4–5 years of training.

You’ve probably suggested this article because Deep Learning thinks you should see it.

Now let’s jump right in!

What is Deep Learning?

Deep learning is an extended arm of machine learning algorithms that teaches computers to do what is inherited naturally to humans i.e. learn by examples.

Artificial Intelligence vs Machine Learning vs Deep Learning, Source

How’s it different from Machine Learning?

The major factor that distinguished Machine Learning & Deep Learning is the data representation and output. Machine Learning algorithms were developed for specific tasks whereas Deep Learning is more of a data representation based upon different layers of a matrix, where each layer utilized the output provided by the previous layer.

The Intuition behind Deep Learning

The sole inspiration for deep learning is to imitate the human brain. It mimics the way the human brain filters out relevant information.

Our brain constitutes billions of biological neurons that are further connected to thousands of biological neurons in order to share and filter information. Deep Learning is a way to recreate that arrangement in a way that works for our machines.

Using Deep learning, we try to develop an artificial neural network.

In our brain, we have _Dendrites, Axon, cell bodies, and Synaptic gaps. T_he signal is passed from the axon(tail of a neuron) to dendrite(head of another neuron) with help of synapse.

Once dendrites get the signals, the cell body does some processing and then send the signal back to the axon, the modified signal is again sent to another neuron and this process repeats over and over again.

To recreate the magic in an artificial neural network, we introduced Inputs, Weights, Outputs, and Activation. A single neuron is useless for us, but when we get a bunch of them, you can recreate the magic. The artificial neural network purely impersonates the working of the natural brain. We feed weighted inputs to the neural layer, some processing happens at the same site that develops an output and which is further fed to the next layer and this happens recursively until we reach the last layer.

#machine-learning #data-science #neural-networks #deep-learning #deep learning