Deep Learning Using TensorFlow | TensorFlow Tutorial | AI & Deep Learning Training | Edureka

Deep Learning Using TensorFlow

Agenda
▪ Why Artificial Intelligence?
▪ What Is Artificial Intelligence?
▪ Subsets Of Artificial Intelligence
▪ What Is Machine Learning?
▪ Limitations Of Machine Learning
▪ What Is Deep Learning And How It Works?
▪ Single Layer Perceptron
▪ Limitations Of Single Layer Perceptron
▪ Multi Layer Perceptron
▪ Course Outline

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Why Artificial Intelligence?
Let’s first understand why we need Artificial Intelligence.

Let’s understand this with an example:
If a car exceeds the speed limit, then for a human to monitor and note down all the
numbers is not possible.

In order to solve it, we can use a machine to capture the
number plate picture and covert it into a text format
Convert the picture into text
UK PL8TE

What Is Artificial Intelligence?
Now is the time to understand what exactly is Artificial Intelligence.

What Is Artificial Intelligence?
Artificial Intelligence is the capability of a machine to imitate
intelligent human behavior.
AI is accomplished by studying how
human brain thinks, and how humans
learn, decide, and work while trying to
solve a problem
Outcomes of this study is used as a
basis of developing intelligent software
and systems.

Subsets Of Artificial Intelligence

Subsets Of Artificial Intelligence
Artificial Intelligence
Machine Learning
Deep Learning 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

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.
Problem Statement: Determine the species of the flower
New Input
Sepal length, Sepal width,
Petal Length, Petal Width
Learn from the dataset

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

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.

Lets Take a Look at
Deep Learning Applications

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
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 to pop up.

Applications of Deep Learning – Machine Translation
Automatic Machine Translation:
• This 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.

Applications of Deep Learning – Image Colorization
Automatic Colorization of Black and White
Images:
• Image colorization is the problem of
adding colour to black and white
photographs.

How Deep Learning Works?

How Deep Learning Works?
Deep learning is a form of machine learning that uses a model of computing that's very much inspired by the
structure of the brain, so lets understand that first.
Neuron
Dendrite: Receives signals from
other neurons
Cell Body: Sums all the inputs
Axon: It is used to transmit signals
to the other cells

Perceptron Learning Algorithm
Let’s begin by understanding an artificial neuron called a perceptron.
X1
X2
X3
Xn
W1
W2
W3
Wn
Transfer
Function
Activation
Function
Schematic for a neuron in a neural net

Applications
It is used to classify any linearly separable set of inputs.
Error = 2 Error = 1 Error = 0

Initialize the weights and
threshold
1
Wj – initial Weight

Provide the input and
calculate the output
threshold
1 2
X – Input
Y - Output

Provide the input and
calculate the output
threshold
Update the weights Repeat step 2 and 3
1 2 3 4
Wj (t+1) = Wj (t) + n (d-y) x
Wj (t+1) – Updated Weight
Wj (t) – Old Weight
d – Desired Output
y – Actual Output
x - Input

Example Of Perceptron

Perceptron Example
It can be used to implement Logic Gates.
AND
X1 X2 Y
0 0 0
0 1 0
1 0 0
1 1 1
t = 1.5
W = 1
W = 1
X1 X2
0 0
0 1
1 0
1 1
1
0 1
X1
X2

Limitations Of Single Layer Perceptron

Limitations Of Single Layer Perceptron
XOR X1 X2 Y
0 0 0
0 1 1
1 0 1
1 1 0
 If the cases are not linearly separable, the learning process of perceptron will never reach a
point where all points are classified properly.
 One example for non linearly separable cases is the XOR problem.
For Solving this problem, a multilayer perceptron
with backpropagation can be used

Multi-Layer Perceptron
A Multi-layer Perceptron has the same structure of a single layer perceptron but with one or more hidden
layers.
Summation:
𝑺 = 𝑤𝑖 ∗ 𝑥1
𝑖 = 1
𝑛
Transformation:
Input Layer
Hidden Layer 1
Hidden Layer 2
Output Layer
Inputs Output

Multi Layer Perceptron Use-Case

Multi Layer Perceptron – Use Case
We will use the MNIST dataset. This dataset contains handwritten images of numbers from 0 -9. This is
how the dataset looks like:
60000 Training Samples
10000 Testing Samples

Multi Layer Perceptron – Use Case
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
Convolutional Layer 1
Convolutional Layer 2
(128 features) (10 features)
We will take the same MNIST dataset. By using Multilayer Perceptron the efficiency can be increased to
99%

Course Outline

Module
1
Module
2
Module
3
Module
4
Module
5
Module
6
Module
7
Module
8
Course Outline
Understanding Deep Learning
Understanding Neural Networks
Master Deep Networks
Deepdive into TensorFlow
Convolutional Neural Networks
Recurrent Neural Networks
RBM and Autoencoders
Certification Project

Session In A Minute
What Is Artificial Intelligence? What Is Machine Learning? What Is Deep Learning?
Deep Learning Applications How Deep Learning Works? Single And Multi Layer Perceptron

AI & Deep Learning with TensorFlow
T e n s o r F l o w W I T H E D U R E K A
Go to www.edureka.co/ai-deep-learning-with-tensorflow

Deep Learning Using TensorFlow | TensorFlow Tutorial | AI & Deep Learning Training | Edureka

Deep Learning Using TensorFlow | TensorFlow Tutorial | AI & Deep Learning Training | Edureka

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