Deep Learning: An In depth Insight and Overview

The human brain never stops amazing us. Be it the way it perceives objects or the way it differentiates them –the brain does all these things very precisely. But the mystery around ‘how the brain performs these functions so accurately’ still keeps lurking.  However, these days we have come up with technologies such as ‘deep learning’ that ape human-intelligence and are based on neural networks. In fact, leading market players like Google have taken great strides by leveraging this new disruptive technology.

So, with the entire buzz around deep learning, machine learning and artificial intelligence being tossed around a lot,the curiosity to know more about them is certainly at its peak. Let’s delve a bit deeper. A sub-field of machine learning, deep learning is all about algorithms encouraged by the functioning of the human brain called artificial neural networks.

Characteristically, a deep neural Network (DNN) is nothing but a multi-layer neural network that tries to identify data patterns. The essence of this method is that as we move forward from the input to the output layers through the hidden layers, the scope of patterns that are being recognized gets larger and larger. This helps in envisaging things at the final layer.

While the general idea or motivation behind deep learning is the way neurons in human brain identify patterns, there are certain features that differentiates simple neural network from deep neural network. These differences are based on architecture, prediction accuracy and flexibility of a Deep Learning model and conventional neural network.

The Need of Deep Learning and Ways to Implement It

For instance, in case of conventional neural networks every node is connected to the other node as we move from one layer to the other. However, the number of nodes taking part in the decision making process can be restricted making it more demonstrative. Furthermore, deep neural network uses back propagation techniques along with feed forward technique resulting in less error at every step.

To understand how deep learning is implemented, it becomes important to understand its architecture, which consists of an input layer, few hidden layers and an output layer. Every layer has nodes that are usually included for making predictions. It is the activation function that determines whether a particular node should be involved in making forecasts or not just like human brain where certain neurons are activated while others aren’t.

In a nutshell, Deep Learning algorithm is all about recognizing the right values to the factors for making accurate predictions. To determine the values, Deep Neural network is made proficient using training data in a way that the entire error measure congregates to its least value.