Introduction to Artificial Intelligence | AI using Deep Learning | Edureka

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Agenda
 What Is Artificial Intelligence ?
 What Is Machine Learning ?
 Limitations Of Machine Learning
 Deep Learning To The Rescue
 What Is Deep Learning ?
 Deep Learning Applications

Applications Of
Artificial Intelligence?
Let’s begin by understanding various un-realistic applications of Artificial Intelligence in real life

Applications Of Artificial Intelligence
Predicting Future

Predicting Future
Chat Bots

Predicting Future
Self-Driving Cars
Chat Bots

Predicting Future Self-Driving Cars
AI Eye Doctor
Chat Bots

AI Eye Doctor
AI Music Composer
Chat Bots

AI Eye Doctor AI Music Composer
AI Dream Machine
Chat Bots

What Is Artificial Intelligence?
Now, let’s understand what is Artificial Intelligence

What Is Artificial Intelligence?
▪ The theory and development of computer systems able to perform tasks normally requiring human
intelligence, such as visual perception, speech recognition, decision-making and translation between
languages.
– source – Wikipedia
▪ AI includes following areas of Specialization:
– Game Playing
– Expert Systems
– Natural Language Processing
– Neural Networks
– Robotics
Machine playing chess
Siri
Number plate recognition

Subsets Of Artificial Intelligence
Deep Learning is a subset of
Machine Learning
Machine Learning is a subset
of AI
Deep Learning uses
neural networks to
simulate human like
decision making
Artificial Intelligence
Machine Learning
Deep
Learning

Machine Learning
Let’s understand what is Machine Learning

Machine Learning
▪ Machine Learning is a type of artificial intelligence (AI) that provide computers with the ability to
learn without being explicitly programmed.
Problem Statement: Determine the species of the flower
Learn from the dataset

Machine Learning
▪ Machine Learning is a type of artificial intelligence (AI) that provide computers with the ability to
learn without being explicitly programmed.
New Input
Sepal length, Sepal width,
Petal Length, Petal Width
Learn from the dataset
Problem Statement: Determine the species of the flower

Limitations Of Machine Learning
Let’s understand, even when Machine Learning is present why we need Deep Learning

Cannot solve crucial AI problems like NLP, Image recognition
etc.
Are not useful while working with high dimensional data, that is
where we have large number of inputs and outputs
Machine
Learning

Cannot solve crucial AI problems like NLP, Image recognition
etc.
Are not useful while working with high dimensional data, that is
where we have large number of inputs and outputs

One of the big challenges with traditional Machine Learning models is a process called feature extraction. For complex
problems such as object recognition or handwriting recognition, this is a huge challenge.
Deep Learning To The Rescue
The idea behind Deep Learning is
to build learning algorithms that
mimic brain.
Deep Learning models are capable to focus on the
right features by themselves, requiring little guidance
from the programmer.
These models also partially solve the dimensionality
problem.

How Deep Learning Works?
Let’s understand the working of Deep Learning

How Deep Learning Works?
 Deep Learning is implemented through Neural Networks.
 Motivation behind Neural Networks is the biological Neuron.
Dendrite: Receives signals from
other neurons
Cell Body: Sums all the inputs
Axon: It is used to transmit
signals to the other cells
X1
X2
X3
Xn
W1
W2
W3
Wn
Transfer
Function
Activation
Function
Schematic for a neuron in a neural netNeuron
Y

What Is Deep Learning?
Input Layer
Hidden Layer 1
Hidden Layer 2
Output Layer
Deep Learning uses Deep Networks which are nothing but Neural Networks with multiple
Hidden Layers.

Deep Learning – Use Case
Let’s look at a use-case where we can use DL for image recognition

Deep Learning Example

Deep Learning Use Case – Hand Written Digits
We will take the same MNIST dataset. By using Multilayer Perceptron the efficiency can be
increased to 99%
Input Image
(28x28)
Filter Weights
(5x5 pixels)
(14x14 pixels)
(16 channels)
(7x7 pixels)
(36 channels)
Filter-Weights
(5x5 pixels)
16 of these …
0.0
0.0
0.1
0.0
0.0
0.1
0.0
0.8
0.0
0.0
0
1
2
3
4
5
6
7
8
9
Fully-Connected
Layer
Output
Layer Class
Layer 1
Layer 2
(128 features) (10 features)

Deep Learning Applications
Let’s look at some applications of Deep Learning

Applications Of Deep Learning
Some amazing and recent applications of Deep Learning are:
• Automatic Machine Translation.
• Object Classification in Photographs.
• Automatic Handwriting Generation.
• Character Text Generation.
• Image Caption Generation.
• Colorization of Black and White Images.
• Automatic Game Playing.
Face recognition
Sara Jessi Amy Priya Adam
EmmaAndrewJohn

Applications Of Deep Learning - Google Lens
▪ Google Lens is a set of vision-based computing
capabilities that allows your smartphone to
understand what's going on in a photo, video or a
live feed.
▪ For instance, point your phone at a flower and
Google Lens will tell you on the screen which type
of flower it is.
▪ You can aim the camera at a restaurant sign to
see reviews and other information.

Applications Of Deep Learning - Machine Translation
▪ This is a task where you are given words in some language and you have to translate the words to the
desired language say English.
▪ This kind of translation is a classical example of Image recognition.

Session In A Minute
Why Artificial Intelligence? What is Artificial Intelligence? Subsets Of Artificial Intelligence
Machine Learning to Deep Learning How Deep Learning Works? Deep Learning Applications

Introduction to Artificial Intelligence | AI using Deep Learning | Edureka

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