Artificial Intelligence - An ingression into Deep Learning.docx

Prelude
Have you ever wondered
How google translates an entire web page to a different language in a matter of seconds? Or, how
your phone gallery groups images into different folders based on their locations?
All these are innovations of Deep Learning! But, what exactly is Deep Learning? Explore to know
more!
Deep Learning
Deep Learning is a subset of Machine Learning, which in turn, is a subset of Artificial Intelligence.
 Artificial Intelligence: Artificial Intelligence (AI) is a technique that helps machines to mimic
human behavior.
 Machine Learning: Machine Learning is an application of AI that allows the system to learn
and improve from experience automatically.
 Deep Learning: Deep Learning is a type of Machine Learning that is inspired by the structure
of the brain. It is also known as Artificial Neural Network (ANN). It uses complex algorithms
and deep neural networks to train models.
What is Deep Learning?
Definition: Deep Learning involves networks which are capable of learning from data and functions
similar to the human brain.
Why Deep Learning?
Let us explore the reasons which make Deep Learning shine in the industry.
 Processes massive amount of data.
o Deep Learning can process an enormous amount of both Structured and Unstructured
data.
 Performs Complex Operations.
o Deep Learning algorithms are capable enough to perform complex operations when
compared to the Machine Learning algorithms.
 Achieves Best Performance.
o As the amount of data increases, the performance of Machine Learning algorithms
decreases.
o On the other hand, Deep Learning maintains the performance of the model.
 Feature Extraction.
o Machine Learning algorithms extract patterns from labeled sample data, while Deep
Learning algorithms take large volumes of data as input, analyze them to extract the
features on its own.

Deep Learning vs. Machine Learning
Say, for instance, we develop a machine that differentiates between cherries and tomatoes.
 Machine Learning: If done through Machine Learning, we need to specify the features based on
which the two can be differentiated like size and stem, in this case.
 Deep Learning: In Deep Learning, the features are picked by the Neural Network without any
human intervention. But, that kind of independence can be achieved by a higher volume of data
in training the machine.
Neural Networks
 The human brain contains billions of cells called Neurons. The structure of a neuron is depicted in
the above image.
 Neural Networks is a set of algorithms designed to learn the way our brain works.
 The biological neurons inspire the structure and the functions of the neural networks.
Biological and Artificial Neurons - Terminologies

Structure of an Artificial Neural Network
The basic structure of an Artificial Neural Network (ANN) consists of artificial neuron that are grouped
into 3 different layers namely:
There are three different layers in a neural network, namely:
 Input Layer
 Hidden Layer
 Output Layer
Input Layer
 The input layer communicates with the external environment and presents a pattern to the
neural network.