Back Propagation Neural Network In AI PowerPoint Presentation Slide Templates Complete Deck

Back Propagation Neural Network
In AI PowerPoint
Presentation
Slide Templates
Your Company Name

Introduction
• What is AI?
• Introduction to AI Levels?
• Types of Artificial Intelligence
• AI VS machine learning vs deep learning
• Where is AI used?
• AI use cases
• Why is AI booming now?
• AI trend in 2020
01
• What is Machine Learning?
• 7 Steps of Machine learning
• Machine Learning vs. Traditional Programming
• How does machine learning work?
• Machine learning Algorithms
• Machine learning use cases
• How to choose Machine Learning Algorithm
• Why to use decision tree algorithm learning
• Challenges and Limitations of Machine learning
• Application of Machine learning
• Why is machine learning important?
Machine Learning
02
Deep Learning
• What is Deep Learning?
• Deep learning Process
• Classification of Neural Networks
• Types of Deep Learning Networks
• Feed-forward neural networks
• Recurrent neural networks (RNNs)
• Convolutional neural networks (CNN)
• Reinforcement Learning
• Examples of deep learning applications
• Why is Deep Learning Important?
• Limitations of deep learning
03
Table of Content
2

Difference between AI vs ML vs DL
• What is AI?
• What is ML?
• What is Deep Learning?
• Machine Learning Process
• Deep Learning Process
• Difference between Machine Learning and Deep Learning
• Which is better to start AI,ML or Deep learning
04
Supervised Machine Learning
• Types of Machine Learning
• What is Supervised Machine Learning?
• How Supervised Learning Works
• Types of Supervised Machine Learning Algorithms
• Supervised vs. Unsupervised Machine learning techniques
• Advantages of Supervised Learning
• Disadvantages of Supervised Learning
05
Unsupervised Machine Learning
• What is Unsupervised Learning?
• How Unsupervised Machine Learning works
• Types of Unsupervised Learning
• Disadvantages of Unsupervised Learning
06
Table of Content
3

Reinforcement Learning
• What is reinforcement learning?
• How reinforcement learning works
• Types of reinforcement learning
• Advantage of reinforcement learning
• Disadvantage of reinforcement learning
07
Back Propagation Neural Network in AI
• Back Propagation Neural Network in AI
• What is Artificial Neural Networks?
• What is Backpropagation?
• Why We Need Backpropagation?
• What is a Feed Forward Network?
• Types of Backpropagation Networks
• Best practice Backpropagation
08
Expert System in Artificial Intelligence
• What is an Expert System?
• Examples of Expert Systems
• Characteristic of Expert System
• Components of the expert system
• Conventional System vs. Expert system
• Human expert vs. expert system
• Benefits of expert systems
• Limitations of the expert system
• Applications of expert systems
09
Table of Content
4

Introduction
01
5
What is AI?
Introduction to AI Levels?
Types of Artificial Intelligence
AI VS machine learning vs deep learning
Where is AI used?
AI use cases
Why is AI booming now?
AI trend in 2020
1
2
3
4
5
6
7
8
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Artificial Intelligence
6
Artificial intelligence (AI) is a popular branch of computer science that concerns with building “intelligent” smart machines capable of performing intelligent
tasks.
With rapid advancements in deep learning and machine learning, the tech industry is transforming radically.
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and capture your audience's attention.
Machine Learning
Deep Learning
Transforming the Nature of Work, Learning, and Learning to Work

Introduction to AI Levels?
7
Artificial Narrow
Intelligence
Artificial General
Intelligence
Artificial Super
Intelligence
Types of
Artificial
Intelligence

Types of Artificial Intelligence
8
01
Deep Learning
02
Machine Learning
03
Artificial
Intelligence

9
2018
2019
2020
2020 2019 2018
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capture your
audience's attention.
65%
35%
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capture your
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capture your
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your needs and
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2016 2017 2018 2019 2020
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your needs and
capture your
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your needs and
capture your
AI
Artificial intelligence (AI) is a popular
branch of computer science that
concerns with building “intelligent”
smart machines capable of performing
intelligent tasks.
With rapid advancements in deep
learning and machine learning, tech
industry is transforming radically.

Machine Learning
10
Machine learning is a type of AI that enables machines to learn from
data and deliver predictive models.
The machine learning is not dependent on any explicit programming
but the data fed into it. It is a complicated process.
Based on the data you feed into machine learning algorithm and the
training given to it, an output is delivered.
A predictive algorithm will create a predictive model.
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Adapt it to your needs and
capture your audience's
attention.
attention.
Information

Deep Learning
11
A computer model can be taught
using Deep Learning to run
classification actions using
pictures, texts or sounds as input
Deep Learning is a subfield
of machine learning that is
concerned with algorithms
inspired by the brain's
structure
functions known as
artificial neural networks
&

AI VS Machine Learning VS Deep Learning
12
• Artificial Intelligence originated
around 1950s
• AI represents simulate
intelligence in machines
• AI is a subset of data science
• Aim is to build machines which
are capable of thinking like
humans
• Machine Learning originated around
1960s
• Machine learning is the practice of
getting machines to make decisions
without being programmed
• Machine learning is a subset of AI &
Data Science
• Aim is to make machines learn
through data so that they can solve
problems
Machine Learning
• Deep Learning originated around
1970s
• Deep Learning is the process of using
artificial neural networks to solve
complex problems
• Deep Learning is a subset of Machine
Learning, AI & Data Science
• Aim is to build neural networks that
au6tonetically discover patterns for
feature detection
Deep Learning

Where is AI used?
13
Supply
Chain
Knowledge
Creation
Customer
Experience
Research &
Development
Human
Resources
Fraud
Detection
Customer
Services
Risk Management &
Analytics
Real-time Operations
Management
Customer
Insight
Predictive
Analytics
Pricing &
Promotion

AI Usecase in HealthCare
14
AI and
Robotics
Keeping Well
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capture your audience's attention.
Early Detection
Diagnosis
Decision Making
Treatment
End of Life Care
Research
Training

AI Use Cases in Human Resource
15
Employee Life Cycle
LEARNING
• Curated Training
• Skill Development
RECRUITING
• Dynamic Career Sites
• Smart Sourcing
ONBOARDING
• Automated Messages
• Curated Videos
ENGAGEMENT
• HR Chatbot
• Engagement Surveys

AI in Banking for Fraud Detection
16
Cardholder
Profiles
Authorization System
Auth Request
Cardholder
Profiles
Payment and Non-Monetary
Transactions
Postings
Payment System
Nonmonetary System
8
1
Auth Request & Score
2
Transaction & Score
5
Case Creation Rules
Execute
6
Case Information
7
Case Creation Module
Analyst
Workstation
Rules Definition
Auth Recommendation
Expert
Rules Base
4
Configuration
Workstation
Expert
Authorization
Response Module
Expert Rules Execute
3
Neural
Network Engine
Scoring Engine

AI in Supply Chain
17
Digital Ecosystem Data Lake
Secure Access Via Identity Management for Transient Users
Pervasive
Visibility
Proactive
Replenishment
Predictive
Maintenance
IIOT – Securely Provisionally People, System and Things
Secure Device
Maintenance
Ecosystem
Integration
Unified
Messaging
Actionable
Insights
Procurement Manufacturing Customers Service
Logistics
Structured & Unstructured
Information
Regulatory
Data
B2B
Transaction
Data
Inventory
Data
Multimedia
Data
Sensor
Data
Logistics
Data
Trading
Partner
Data
Social
Media
Data

Ai Chatbots in Healthcare
18
Search Engine
Users learn to search for
information
Social Platforms
Like Facebook connect
users online
Smartphones
Bring the internet online
App Eco-system
Lets users download and
use apps easily
Messenger Apps
Lets users chat anywhere,
anytime
Self Learning machines
becomes smarter the more
they are used
Healthbots
Bring all of the above
together for healthcare use
cases

Why is AI booming now?
19
Global AI Revenue Forecast by 2025, Ranked by Use Case in millions US Dollar
8,097 7,540 7,336
4,680
4,201
3,714 3,655 3,564 3,169
0
2,000
4,000
6,000
8,000
Static Image
Recognition,
Classification and
Tagging
Algorithm Trading
Strategy Performance
Management
Efficient, Scalable
Processing of Patient
Data
Predictive
Maintenance
Object Identification,
detection,
classification,
tracking
Text Query of Images Automated
Geophysical Feature
Detection
Content Distribution
on Social Media
Object detection &
classification,
avoidance, navigation
100% 92% 84%
45%
54%
United
States
China India
Germany
Israel
22%
24%
27%
36%
37%
51%
60%
60%
66%
71%
0 20 40 60 80 100
Fleet Mobile
Expediting Transactions
Production Floor Systems
Logistics & Supply Chain
Monitoring Through External Devices/Systems
Operational Environment
HR/Workforce Management
Security/Fraud
Customer Relation/Interaction (i.e., chatbots)
External Communication
Organizations deploying AI, by Functional Areas
Penetration of Artificial Intelligence Skills, by Country

10 AI Trend in 2020
20
AI Trends”
“2020
Robotic Process
Automation
AI will make Healthcare more
Accurate
Data Modeling will
move to the Edge
AI will Come for
B2B
Ai-powered
Chatbots
Automated Business
Process
AI In Retail
Aerospace and Flight
Operations Controlled by AI
AI Mediated Media and
Entertainment
Advanced
Cybersecurity

21
Machine Learning
02
What is Machine Learning?
7 Steps of Machine learning
Machine Learning vs. Traditional Programming
How does machine learning work?
Machine learning Algorithms
Machine learning use cases
How to choose Machine Learning Algorithm
Why to use decision tree algorithm learning
Challenges and Limitations of Machine learning
Application of Machine learning
Why is machine learning important?
1
2
3
4
5
6
7
8
9
10
11

Machine Learning
22
Traditional
Programming
Data
(Input)
Program
Output
Data
(Input)
Output
Machine
Learning
Program
Machine Learning is the result of General AI that involves developing machines that can deliver
results better than humans
“Learning”
Machine
Learning System
Input Data:
Feed Learner
Various Data
Output Data:
Present Rules

7 Steps of
Machine Learning
23
Gathering
Data
Preparing
that Data
Choosing a
Model
Training
Evaluation
Hyperparameter
Tuning
Prediction
1
2
3
4
5
6
7

Machine Learning vs. Traditional Programming
24
Prediction
Result
Computer
Data
Handcrafted
Model
Learning
Model
Prediction
Result
New Data
Model
Sample Data
Expected
Result
Computer
Computer
Traditional Modelling
Machine Learning

How does Machine Learning Work?
25
Collect data from hospitals,
health insurance companies,
social service agencies, police
and fire dept.
Collect Data
Depend on the problem to be solved
and the type of data an appropriate
algorithm will be chosen.
Select Algorithm The remaining data is utilized to test the model,
for accuracy. Depending on the results,
improvements, can be performed in the “Train
model’ and/or “Select Algorithm” phases,
iteratively.
Test Model
Identify, the problem to
be solved and create a
clear objective.
Define Objectives
Preparing data is a crucial step
and involves building workflows
to clean, match and blend the
data.
Prepare Data
Data is fed as input and the algorithm
configured with the required parameters.
A percent of the data can be utilized to
train the model.
Train Model
Publish the prepared
experiment as a web
service, so applications can
use the model.
Integrate Model

Machine Learning Algorithms
26
Reinforcement
Supervised Unsupervised
Classification
• Linear
• Polynomial
Regression
Decision Tree
Random
Forest
Association
Anlaysis
• KNN
• Trees
• Logistic Regression
• Naïve-Bayes
• SVM
Regression Clustering
• SVD
• PCA
• K-means
Continuous
Categorical
• Apriori
• FP-Growth
Machine Learning
Hidden Markov
Model

Machine Learning Use Cases
27
Energy Feedstock & Utilities
• Power Usage Analytics
• Seismic Data Processing
• Your Text Here
• Smart Grid Management
• Energy Demand & Supply Optimization
Financial Services
• Risk Analytics & Regulation
• Customer Segmentation
• Your Text Here
• Credit Worthiness
Evaluation
Travel & Hospitality
• Aircraft Scheduling
• Dynamic Pricing
• Your Text Here
• Traffic Patterns & Congestion
Management
Manufacturing
• Predictive Maintenance or Condition
Monitoring
• Your Text Here
• Demand Forecasting
• Process Optimization
• Telematics
Retail
• Predictive Inventory
Planning
• Recommendation Engines
• Your Text Here
• Customer ROI & Lifetime
Value
Healthcare & Life Science
• Alerts & Diagnostics from Real-time
Patient Data
• Your Text Here
• Predictive Health Management
• Healthcare Provider Sentiment
Analysis

How to Choose Machine Learning Algorithm
28
How to Select Machine Learning Algorithms
Accuracy Linearity Number of
Parameters
Training
Time
Number of
Features
Algorithm Cheat Sheet
Additional
Requirements
What do you want to
do with your Data?

Why use Decision Tree Machine Learning Algorithm?
29
Decision
Trees
To Classify
Use Standard
Classification
here
Use c4.5
Implementation
Responsible
Variable has only 2
Categories
Response
Variable has
Multiple
Categories
To Predict
Responsible
variable is
Continuous
Linear
Relationship
between
Predictors
& Response
Non-linear
Relationship
between
Predictors &
Response
Use Standard
Regression
Tree
Use c4.5
Implementation

Challenges and Limitations of Machine learning
30
Handling multi-dimensional &
multi-variety Data
Easily Identifies Trends and Patterns
No Human Intervention needed
Continuous Improvement
Wide Applications
High error-
Susceptibility
Data
Acquisition
Time and
Resources
Interpretation
Results
Advantages Disadvantages

Application of Machine Learning
31
Virtual Personal Assistant
Medical Diagnosis
Stock Market Trading
Online Fraud Detection
Email Spam and Malware Filtering
Self Driving Cars
Product Recommendations
Traffic Prediction
Speech Recognition
Image Recognition
Automatic Language
Translation

Why is Machine Learning Important?
32
Phase 1 : Learning
Training
Data
Phase 2 : Prediction
Prediction Predicted Data
Model
New Data
• Normalization
• Dimension Reduction
• Image Processing,
etc.
Pre-Processing
• Precision/recall
• Over fitting
• Test/cross Validation
data, etc.
Error Analysis
• Supervised
• Unsupervised
• Minimization, etc.
Learning

What is Deep Learning?
Deep learning Process
Classification of Neural Networks
Types of Deep Learning Networks
Feed-forward neural networks
Recurrent neural networks (RNNs)
Convolutional neural networks (CNN)
Examples of deep learning applications
Why is Deep Learning Important?
Limitations of deep learning
33
1
2
3
4
5
6
7
8
9
10
11
Deep Learning
03

34
A computer model can be taught using Deep Learning to run
classification actions using pictures, texts or sounds as input.
Deep Learning is a subfield of machine learning that is concerned with
algorithms inspired by the brain's structure and functions known as
artificial neural networks.
Feature Extraction +
Classification
Car
Not Car
Output
Input

Deep Learning Process
35
Select Deep Learning
Algorithms
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to your needs and capture your
Understand
the Problem
Test the
Model
Training
the Model
Identify
Data

Classification of Neural Networks
36
x1
x2
xn
v11
v12
vpn
w11
w22
wmp ym
y2
y1
V1n
w1p
Input Layer Hidden Layer Output Layer
1
2
1
2
p m

Types of Deep Learning Networks
37
• Artificial Neural Networks (ANN)
• Convolutional Neural Networks
(CNN)
• Recurrent Neural Networks (RNN)
• Self Organizing Maps (SOM)
• Boltzmann Machines (BM)
• AutoEncoders (AE)
Deep Learning
Models
Supervised Unsupervised
Supervised
Artificial Neural Networks Used for Regression & Classification
Convolutional Neural Networks Used for Computer Vision
Recurrent Neutral Networks Used for Time Series Analysis
Unsupervised
Self-Organizing Maps Used for Feature Detection
Deep Boltzmann Machines Used for Recommendation Systems
AutoEncoders Used for Recommendation Systems

Feed-forward Neural Networks
38
Variable- #1
Variable- #2
Variable- #3
Variable- # 4
Output
Output
Layer
Input
Layer
Hidden
Layer

Recurrent Neural Networks (RNNs)
39
Recurrent Network
Input Layer
Output Layer
Hidden Layers
X 1
X 2
Y

Convolutional Neural Networks (CNN)
40
Take Car = A1 Truck = B1 VAN = C1 Bicycle = D1 Rest all be Same

41
Agent Environment
Action
State, Reward
uses rewards and punishment to train computing models to
perform a sequence of selections. Here computing faces a
game-like scenario where it employs trial and error to
answer. Based on the action it performs, computing gets
either rewards or penalties. Its goal is to maximize the
rewards.
Exploration
Policy
Neural
Networks
Filters Memory
Algorithm

Examples of Deep Learning Applications
42
Image
Recognition
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your needs and capture your
Portfolio Management & Prediction of
Stock Price Movements
Speech
Recognition
Natural Language
Processing
Drug Discovery & Better Diagnostics of
Diseases in Healthcare
Robots and Self -
Driving Cars

Why is Deep Learning Important?
43
Performance
Data
Deep
Learning
Other
Learning
Algorithms

Limitations of Deep Learning
44
Limitations of
Deep Learning
Interpretability
attention.
Statistical
Reasoning
attention.
Amount of Data
attention.

Which is better to start AI,ML or Deep learning
What is AI?
What is ML?
Machine Learning Process
Difference between Machine Learning and Deep Learning
Difference between
AI vs ML vs DL
04
45
1
2
3
4
5
6
7

Difference between AI vs ML vs DL
46
Ability to learn without being
explicitly programmed
Machine Learning
Engineering of making intelligent
machines and programs
Learning based on deep
neural network
Deep Learning

What is AI?
47
(AI) is a popular branch of computer
science that concerns with building
“intelligent” smart machines capable of
performing intelligent tasks.
With rapid advancements in deep
learning and machine learning, the
tech industry is transforming
radically.
Artificial
intelligence
Artificial
Intelligence

What is ML?
48
Introduction to Machine learning
Ordinary System With AI Machine Learning
Learns
Predicts
Improves
Machine Learning
is a type of AI that enables machines to learn from data and deliver predictive models. The
machine learning is not dependent on any explicit programming but the data fed into it. It is a
complicated process. Based on the data you feed into machine learning algorithm and the
training given to it, an output is delivered. A predictive algorithm will create a predictive
model.

49
Artificial intelligence (AI) is a popular branch of computer
science that concerns with building “intelligent” smart
machines capable of performing intelligent tasks.
With rapid advancements in deep learning and machine
learning, the tech industry is transforming radically.

Machine Learning Process
50
Data
Gathering
Data
Cleaning
Building
Model & Finalising
Data Transformation
into Predictions
Selecting
Right Algorithms
Data
Raw & Training Data
Modelling
Candidate & Final
Visualisation
Predictions & Strategy
Machine
Learning
Process

51
Understand the
Problem
Select Deep Learning
Algorithm
Test the Model
Identify Data
Training the Model

Difference between Machine Learning and Deep Learning
52
Input Feature
Extraction
Classification Output
Car
Not Car
Input Feature Extraction+ Classification
Car
Not Car
Output
Machine
Learning
Deep Learning

Which is better to start AI,ML or DL?
53
Any Technique which enables computers to
mimic human behavior.
Subset of ML which make the Computation
of Multi-layer Neural Networks Feasible.
Deep Learning
Subset of AI Techniques which use Statistical
Methods to Enable Machines to Improve with
Experiences.
Machine Learning

54
1
2
3
4
5
6
7
Supervised
Machine Learning
05
Disadvantages of Supervised Learning
Types of Machine Learning
What is Supervised Machine Learning?
How Supervised Learning Works
Types of Supervised Machine Learning Algorithms
Supervised vs. Unsupervised Machine learning techniques
Advantages of Supervised Learning

Types of Machine Learning
55
Supervised
Learning
Makes Machine Learn Explicitly
Data with Clearly defined Output is
given
Direct feedback is given
Predicts outcome/future
Resolves Classification and Regression
Problems
Inputs Outputs
Training
Unsupervised
Learning
Machine Understands the data
(Identifies Patterns/ Structures)
Evaluation is Qualitative or Indirect
Does not Predict/Find anything Specific
Inputs Outputs
Reinforcement
Learning
An approach to AI
Reward Based Learning
Learning form +ve & +ve
Reinforcement
Machine Learns how to act in a Certain
Environment
To Maximize Rewards
Inputs Outputs
Rewards

What is Supervised Machine Learning?
56
Supervised Learning
Processing
Algorithm
Input Raw Data Output
Training
Data set
Desired
Output
Supervisor

How Supervised Machine Learning works
57
Step 1 Provide the Machine Learning Algorithm Categorized
or “labeled” Input and Output Data from to Learn
Step 2 Feed the Machine New, Unlabeled Information to See if it Tags
New Data Appropriately. If not, Continue Refining the Algorithm
Label
“Group 1”
Machine Machine
Group 1
Group 2
Types of Problems to which it’s Suited
Classification
Sorting Items into Categories
Regression
Identifying Real Values
(Dollars, Weight, etc.)

Supervised
Learning
Classification
• Fraud Detection
• Email Spam Detection
• Diagnostics
• Image Classification
Regression
• Risk Assessment
• Score Prediction
Types of Supervised Machine Learning Algorithms
58

VS
Supervised
Learning
• Classification
• Regression
Input & Output
Data
Unsupervised
Learning
• Clustering
• Association
Input
Data
Predictions & Predictive
Models
Patterns / Structure
Discovery
Supervised vs. Unsupervised Machine Learning Techniques
59

Advantages of Supervised Learning
Advantages
It allows you to be very specific about the definition of the labels. In other
words, you'll train the algorithm to differentiate different classes where you'll set a
perfect decision boundary.
You are ready to determine the amount of classes you would
like to possess.
The input file is extremely documented and is
labeled.
The results produced by the supervised method are more accurate and reliable as
compared to the results produced by the unsupervised techniques of machine
learning. this is often mainly because the input file within the supervised algorithm
is documented and labeled. this is often a key difference between supervised and
unsupervised learning.
The answers within the analysis and therefore the output of your algorithm
are likely to be known thanks to that each one the classes used are known.
This slide is 100% editable. Adapt it to your needs and capture your audience's attention 60

• Supervised learning are often a posh method as compared with the unsupervised
method. The key reason is that you simply need to understand alright and label the
inputs in supervised learning.
• It doesn’t happen in real time while the unsupervised learning is about the
important time. this is often also a serious difference between supervised and
unsupervised learning. Supervised machine learning uses of-line analysis.
• It is needed tons of computation time for training.
• If you've got a dynamic big and growing data, you're unsure of the labels to
predefine the principles. this will be a true challenge.
Disadvantages of Supervised Learning
61

What is Unsupervised Learning?
How Unsupervised Machine Learning works
Types of Unsupervised Learning
Disadvantages of Unsupervised Learning
Unsupervised
Machine Learning
06
62
1
2
3
4

Unsupervised
Learning
Algorithm
Interpretation Processing
• Unknown output
• No Training Data Set
What is Unsupervised Learning?
63

Step 1 Provide the machine learning algorithm uncategorized,
unlabeled input data to see what patterns it finds
Step 2 Observe and learn from the patterns the
machine identifies
Machine Machine
Similar Group 1
Similar Group 2
Types of Problems to Which it’s Suited
Anomaly Detection
Identifying abnormalities in data
For Example: Is a hacker intruding in
our network?
Clustering
Identifying similarities in groups
For Example: Are there patterns in the data to indicate
certain patients will respond better to this treatment
than others?
How Unsupervised Machine Learning works
64

Unsupervised
Learning
Dimensionality
Reduction
• Text Mining
• Face Recognition
• Big Data Visualization
• Image Recognition
Clustering
• Biology
• City Planning
• Targeted Marketing
Types of Unsupervised Learning
65

Disadvantages of Unsupervised Learning
66
You cannot get very specific about the definition of the info sorting and
therefore the output. This is often because the info utilized in unsupervised
learning is labeled and not known. It's employment of the machine to label
and group the data before determining the hidden patterns.
Less accuracy of the results. This is often also because the input
file isn't known and not labeled by people beforehand , which
suggests that the machine will got to do that alone.
The results of the analysis can't be ascertained. there's no prior knowledge within
the unsupervised method of machine learning. Additionally, the numbers of classes also
are not known. It results in the lack to determine the results generated by the analysis.

What is reinforcement learning?
How reinforcement learning works
Types of reinforcement learning
Advantage of reinforcement learning
Disadvantage of reinforcement learning
Reinforcement
learning
07
67
1
2
3
4
5

Response Feedback Learns
It’s a
mango
Wrong!
It’s an
apple
Noted
It’s an
Apple
Reinforced
Response
Input
Input
What is Reinforcement Learning?
68

Reinforcement
Learning
Environment
Agent
Reward
Best Action
State
Selection of
Algorithm
How Reinforcement Learning Works?
69

Gaming
Finance Sector
Manufacturing
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needs and capture your audience's attention.
Inventory Management
Robot Navigation
Types of Reinforcement Learning
70

Disadvantage of Reinforcement Learning
71
You cannot get very specific
about the definition of the
info sorting and therefore
the output. this is
often because the info utilized
in unsupervised learning is
labeled and not
known. it's employment of the
machine to label and group
the data before determining the
hidden patterns.
Less accuracy of the results. this is often also
because the input file isn't known and not
labeled by people beforehand , which
suggests that the machine will got to do
that alone. The results of the analysis can't
be ascertained. There's no prior
knowledge within the unsupervised method
of machine learning. Additionally, the
numbers of classes also are not known.
It results in the lack to determine the results
generated by the analysis Reinforcement
learning as a framework is wrong in many
various ways, but it's precisely this
quality that creates it useful.
Too much reinforcement learning
can cause an overload of
states which may diminish the
results.
Reinforcement
learning isn't preferable
to use for solving simple
problems.
Reinforcement learning needs tons of
knowledge and tons of
computation. it's data-
hungry. that's why it works rather
well in video games because one can
play the sport again and again and
again, so getting many data seems
feasible.

72
1
2
3
4
5
6
7
Back Propagation Neural
Network in AI
08
Best practice Backpropagation
What is Artificial Neural Networks?
What is Backpropagation?
Why We Need Backpropagation?
What is a Feed Forward Network?
Types of Backpropagation Networks

73
w1
b1 b2
i1
i2
1
h2
2
net out

What is Artificial Neural Networks?
74
Feed-Forward
Input Layer
Hidden Layer
Back Propagation
Output Layer
Network Output
Network Inputs

What is Backpropagation Neural Networking
75
Difference in
Desired Values
Backprop Output Layer
Input Layer
Hidden Layer(s)
Output Layer
x
x
x
w
w
w
w
1
1
3
5

Most prominent
advantages of
Backpropagation
Are:
Backpropagation is fast,
simple and straightforward to
program.
It has no parameters to
tune aside from the
numbers of input.
It is a versatile method because
it doesn't require prior knowledge
about the network.
It doesn't need any special
mention of the features of the
function to
be learned.
It is a typical method
that generally works
well.
Why We Need Backpropagation?
76

What is a Feed Forward Network?
77
Input
Layer
Hidden
Layer
Output
Layer

• Static Back-propagation
• Recurrent Backpropagation
Recurrent backpropagation is fed forward until a hard and
fast value is achieved. Then, the error is computed and
propagated backward.
It is one quite backpropagation network which produces a
mapping of a static input for static output. it's useful to
unravel static classification issues like optical character
recognition.
Static Back-propagation
Recurrent Backpropagation
The main difference between both of those methods is: that the mapping is rapid in static back-
propagation while it's nonstatic in recurrent backpropagation
Types of Backpropagation Networks
78

A neural network is a group of connected it I/O units where
each connection features a weight related to its computer
programs.
Backpropagation may be a short form for "backward
propagation of errors." it's a typical method of coaching artificial
neural networks.
Backpropagation is fast, simple and straightforward to
program.
A feedforward neural network is a man-made neural
network.
Best Practice Backpropagation
79
BACKPROPAGATION

Benefits of expert systems
What is an Expert System?
Examples of Expert Systems
Characteristic of Expert System
Components of the expert system
Conventional System vs. Expert system
Human expert vs. expert system
Limitations of the expert system
Applications of expert systems
80
1
2
3
4
5
6
7
8
9
Expert System in
09

Expert System in Artificial Intelligence
81
Knowledge Base
Inference
Engine
User
Interface
User
(May not be an expert)
Knowledge
Engineer
Human
Expert
The Expert System in AI are
computer applications. Also,
with the assistance of this
development, we will solve
complex problems. it's level of
human intelligence and expertise

Examples of Expert Systems
82
The Highest Level of Expertise
• The expert system offers the very best level of experience. It
provides efficiency, accuracy and imaginative problem-solving.
Right on Time Reaction
• An Expert System interacts during a very reasonable period of your time
the user the entire time must be but the time taken by an expert to urge
the foremost accurate solution for an equivalent problem .
Good Reliability
• The expert system must be reliable, and it must not make
any an error.
Flexible
• It is significant that it remains flexible because it the is
possessed by an Expert system.
Effective Mechanism
• Expert System must have an efficient mechanism to administer
the compilation of the prevailing knowledge in it.
Capable of Handling Challenging Decision & Problems
• An expert system is capable of handling challenging decision
problems and delivering solutions.
Non-expert
User
Query
Expert System
Inference
Engine
Knowledge from
an expert
User
Interface
Knowledge
Base
Advice

• Expert systems use symbolic
representations for knowledge (rules,
networks or frames) and perform their
inference through symbolic computations
that closely resemble manipulations
of tongue
Use Symbolic Representations
• The system should be designed in
such how that it's ready to perform
within alittle amount of your time , like or better than
the time taken by a person's expert to succeed in at a
choice point. An expert system that takes a year to
succeed in a choice compared to a person's expert’s
time of 1 hour wouldn't be useful
Adequate Response Time
• The system should be understandable
i.e. be ready to explain the steps of
reasoning while executing. The expert
system should have an
evidence capability almost like the
reasoning ability of human experts
Understandable
• The expert system must
be as reliable as a
person's expert
Good Reliability
• Expert systems are typically very domain specific. For
ex., a diagnostic expert system for troubleshooting
computers must actually perform all the required data
manipulation as a person's expert would. The
developer of such a system must limit his or her scope
of the system to only what's needed to unravel the
target problem. Special tools or programming
languages are often needed to accomplish the
precise objectives of the system
Domain Specificity
• The system must be capable of responding at A
level of competency adequate to or better than
an expert system within the field. the
standard of the recommendation given by the
system should be during a high level integrity
and that the performance ratio should be also
very high
High level Performance
Characteristic of Expert System
83

Inference
Engine
Explanation
Knowledge
Base
Acquisition
Facility
User
Interface
Experts and
Knowledge Engineers
Users
Components of the Expert System
84

Knowledge domain break
away the mechanism processing
The program could
have made an error
Not necessarily need all
the input/data
Changes within the rule are
often made with ease
The system can work only
with the rule as a tittle
Information and processing
combined during a sequential file
The
program isn't wrong
Need all
the input file
Changes to the program are
inconvenient
The system works if it's
complete
01
02
03
04
05
02
03
04
05
01
vs
Conventional System vs. Expert System
85

Human Experts (Artificial ) Expert Systems
Perishable
Difficult to Transfer
Difficult to Document
Expensive, especially top notch
Add Your Text Here
Permanent
Easy to Transfer
Easy to Document
Affordable, costly to develop, but cheap to operate
Add Your Text Here
Human Expert vs. Expert System
86

Fast
Response
Easy to Develop
and Modify the
System
Humans
Emotions are
not Affected
Error Rate
are Very
Low
Low
Accessibility
Cost
Benefits of Expert Systems
87

Don’t Have Decision Making
Power Like Humans
Expert System is not Widely
used or Tested
It cant Deal with the Mixed
Knowledge
There are Chances of
Errors
Its Difficult to Maintain
Its Developed for a Specific
Domain
Not Able to Explain the
Logic Behind the Decision
Development Cost is
High
Limitations of the Expert System
88

Applications of Expert Systems
89
Monitoring system
Design domain (Camera lens
design,automobile design)
Medical domain (Diagnosis system,
medical operations)
Repairing
Warehousing
Optimization
Process Control System
Finance/Commerce (Stock market trading,
airline scheduling cargo scheduling)
Shipping
Knowledge domain (Finding out
the faults in vehicles, computer)

90
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