This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
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10. Image Recognition
Lets understand how Artificial Neural Network identifies the images
Training Data Feature Extraction
Artificial Neural Network
11. Image Recognition
Lets understand how Artificial Neural Network identifies the images
Training Data Feature Extraction
Artificial Neural Network
Artificial Neural Network
Test Data
12. Image Recognition
Lets understand how Artificial Neural Network identifies the images
Training Data Feature Extraction
Identifies the Dogs
Artificial Neural Network
Artificial Neural Network
Test Data
13. What’s in it for you?
What is Deep Learning?
Why do we need Deep Learning?
Applications of Deep Learning
What is a Neural Network?
Activation Functions
Working of a Neural Network
15. What is Deep Learning?
Deep Learning is a subfield of Machine Learning that deals with algorithms inspired
by the structure and function of the brain
Artificial Intelligence
Machine
Learning
Deep
Learning
Ability of a machine to imitate
intelligent human behavior
Application of AI that allows a system
to automatically learn and improve
from experience
Application of Machine Learning that
uses complex algorithms and deep
neural nets to train a model
16. Why do we need Deep Learning?
Process huge amount of data
Machine Learning algorithms work with huge amount of structured data but Deep Learning algorithms
can work with enormous amount of structured and unstructured data
Perform complex algorithms
Machine Learning algorithms cannot perform complex operations, to do that we need Deep Learning
algorithms
To achieve the best performance with large amount of data
As the amount of data increases, the performance of Machine Learning algorithms decreases, to
make sure the performance of a model is good, we need Deep Learning
Feature Extraction
Machine Learning algorithms extract patterns based on labelled sample data, while Deep Learning
algorithms take large volumes of data as input, analyze the input to extract features out of an object
and identifies similar objects
18. Applications of Deep Learning
Cancer Detection
Deep Learning helps to detect cancerous tumors in the human body
19. Applications of Deep Learning
Robot Navigation
Deep Learning is used to train robots to perform human tasks
20. Applications of Deep Learning
Autonomous Driving Cars
Distinguishes different types of objects, people, road signs and drives
without human intervention
21. Applications of Deep Learning
Machine Translation
Given a word, phrase or a sentence in one language, automatically
translates it into another language
22. Applications of Deep Learning
Music Composition
Deep Neural Nets can be used to produce music by making computers
learn the patterns in a composition
23. Applications of Deep Learning
Colorization of images
Uses the object and their context within the photograph to color the image
25. What is a Neural Network?
Deep Learning is based on the functioning of a human brain, lets understand how does a
Biological Neural Network look like
Dendrite
Cell Nucleus
Synapse
Axon
26. What is a Neural Network?
Deep Learning is based on the functioning of a human brain, lets understand how does an
Artificial Neural Network look like
Neuron
X1
X2
Xn
Input 1
Input 2
Input n
Y Output
w1
w2
wn
28. What is a Neural Network?
First step in the process is to calculate the weighted sum of the inputs and add a bias
X1
X2
Xn
Input 1
Input 2
Input n
w1
w2
w
Step 1
i=1
n
w x + b
i i*
n
Transfer Function
29. What is a Neural Network?
Second step in the process is to pass the calculated weighted sum as input to the activation
function to generate the output
X1
X2
Xn
Input 1
Input 2
Input n
Y
Output
w1
w2
w
Step 1
i=1
n
w x + b
i i*
n
Step 2
Activation FunctionTransfer Function
30. Activation Functions
An Activation function takes the “weighted sum of input plus the bias” as the input to the
function and decides whether it should be fired or not
Types of Activation
Functions
Sigmoid Function
Threshold Function
ReLU Function
Hyperbolic Tangent
Function
31. Activation Functions
Sigmoid Function
Used for models where we have to predict the probability as an output. It exists between 0 and
1.
i=1
n
w x + b
i i*
0
1
Y
(X)=
1
1 + e-x
32. Threshold Function
Activation Functions
It is a threshold based activation function. If Y value is greater than a certain value, the function
is activated and fired else not.
i=1
n
w x + b
i i*
0
1
Y
(X)=
1, if x>=0
0, if x<0( )
33. ReLU Function
Activation Functions
It is the most widely used Activation function and gives an output of X if X is positive and 0
otherwise
i=1
n
w x + b
i i*
0
1
Y
(X) = max(X,0)
37. Working of a Neural Network
Lets understand how a neural network classifies the images of Dogs and Cats
How to identify
Dogs and Cats?
Feed the images of Dogs and Cats to the
neural network as input
Artificial Neural Network
38. Working of a Neural Network
Lets understand how a neural network classifies the images of Dogs and Cats
X
X
1
n
X2
Y^
Classifies Dogs and Cats
Cats
Dogs
Hidden Layer Hidden Layer
39. Working of a Neural Network
Lets consider a simple neural network
X
X
Y
Y1
2
n
w
1
wn
i=1
m
w x
i i*( ) ^ Model Output
Actual Output
Bias
Forwardpropagation
Compare predicted
output with actual outputX2
w2
40. Working of a Neural Network
After training the Neural Network, it uses Backpropagation method to improve the performance of the
network. Cost Function helps to reduce the error rate.
X
X
X
Y
Y1
2
n
w
1
wn
i=1
m
w x
i i*( ) ^ Model Output
Actual Output
C = ½( Y – Y )^
2
Cost Function:
w2
Bias
41. Cost Function
The Cost value is actually the difference between the neural nets predicted output and the actual output from a
set of labelled training data. The least cost value is obtained by making adjustments to the weights and biases
iteratively throughout the training process.
X
X
Y
Y1
n
w
1
w
w2
n
i=1
m
w x
i i*( ) ^ Model Output
Actual Output
C = ½( Y – Y )^
2
Cost Function:
Bias
X2
42. Why are Deep Neural Nets hard to train?
Training
Gradient is the rate at which cost changes with
respect to weight and bias
Until 2006, there was no proper method to accurately train
deep neural networks due to a basic problem with the
training process:
The Vanishing Gradient
43. Why are Deep Neural Nets hard to train?
Let’s understand Gradient like a slope and the training process like Rolling a ball
Slower Faster
The ball will roll slower if the slope is gentle and will roll faster if the slope is steep. Likewise, a Neural Net will
train slowly if the Gradient is small and it will train quickly if the Gradient is large.
50. Neural Network Prediction
Backpropagation
2
C = ½( Y – Y )
X
X
1
3
Y^
Adjusting the weights and biases using backpropagation
method to improve the model
X2
Y
Actual Output
Cost Function: