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.
