Artificial Intelligence and Deep Learning for Decision Makers
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Description
Contents
Reviews
Language
English
ISBN
9789389328684
Cover Page
Title Page
Copyright Page
Dedication
About the Authors
About the Reviewer
Acknowledgements
Preface
Errata
Table of Contents
1. Artificial Intelligence and Deep Learning
Structure
Objective
Artificial intelligence (AI)
Importance of AI
Capabilities of AI
Deep learning (DL)
Machine learning versus deep learning
Current scenario on machine learning
The current scenario in deep learning
The exponential explosion of available data
The rise of the Graphics Processing Unit (GPU)
The invention of advanced algorithms
Deep learning and Big Data
Introduction to Artificial Neural Networks (ANNs)
The single neuron of humans
Detailed working for ANN
ANN architecture
Types of neural networks in AI
Neural network architecture types
Algorithmic problem-solving approach
Preprocessing
Dimensionality reduction
Scaling
Feature selection
Model selection
Cross-validation
Performance metrics
Hyperparameter optimization
Evaluation of model and predicting patterns
Collection of data
Preprocessing of data
Preparation of data
Training of data
Steps of implementation
Testing of model
Conclusion
Questions
2. Data Science for Business Analysis
Structure
Objective
What is data science?
Challenges faced by businesses
Uncertainty
Globalization
Innovation
Government policy and regulation
Diversity
Complexity
Technology
Supply chains
Strategic thinking and problem solving
Information overload
Problems that occur during model development
Design and development of models
Problems faced by IT organizations while developing models
Artificial intelligence and deep learning methods to develop models
Artificial intelligence (AI)
Deep learning (DL)
Why DL matters?
Usage of deep learning
Improvements in the business
DL to optimize manufacturing
Time series analysis for business forecasting
DL in bot recommendation
Predictive and preventive maintenance for industrial IoT
Deep learning in security
Deep learning in healthcare
Fraud detection with deep learning neural network
Benefits of data science in business analysis
Conclusion
Questions
3. Decision Making
Structure
Objective
Representation of problems
Design and development knowledge representation
Types of knowledge
Representation
Knowledge engineering
Representation techniques
Knowledge representation using predicate logic
Knowledge representation using semantic net
Knowledge representation using frames
Knowledge representation using scripts
Knowledge representation issues
Mathematical formulations of representing knowledge
Model representation
Analyze real-world problem
Strategies for searching possible solutions from the problem spaces
Solution strategy
Designing Uber maps
Uber service architecture
Conclusion
Questions
4. Intelligent Computing Strategies by Google
Structure
Objective
The strategies of Google in deep learning exploration
Research environment by DeepMind and other services provided to the users
AlphaGo
Autonomous cars
Working of autonomous car
Google Play
DeepMind Health Stream application
AI navigation without a map
Deep Q-Network (DQN)
Working of DQN
Business models currently adopted by Google
Google business model canvas
How Google will impact current businesses?
Google AutoML
DeepLab-v3+
DeepMind
WaveNet
Tensor Processing Unit (TPU)
Conclusion
Questions
5. Cognitive Learning Services in IBM Watson
Structure
Objective
The cognitive learning in NLP
Cognitive computing
Features required for a cognitive system
Evolution of cognitive system
Characteristics of cognitive computing
Difference between artificial intelligence and cognitive computing
The scope of cognitive computing and systems
Use of cognitive computing in NLP
Applications of cognitive computing
Issues in cognitive aspects of language modeling
Cognitive computing landscape
IBM Watson
Watson solutions
IBM Watson Explorer
Working with Watson Explorer
Improving services with IBM Watson
Government
Law enforcement
Financial services
Banking
Insurance
Healthcare
Retail
Customer domain
Product domain
How to impact businesses with IBM Watson
Watson Explorer for manufacturing
Watson Explorer for customer service and call-center
Watson Explorer for retail and e-commerce
Watson Explorer for insurance
Conclusion
Questions
6. Advancement of Web Services by Baidu
Structure
Objective
Baidu web services and its business orientation
Market share of Baidu
Tools for Baidu
Difference between Baidu and Google
Business model of Baidu
Product strategy
Get the best ranking in the Baidu Search Engine
Research and technology
SWOT analysis
Deep learning in Baidu web services
Key assets of Baidu
Uses of Baidu
Major problems of Baidu
Uncertain quality of search results
Problems in Baidu mobile promotion
Next steps of Baidu intelligent web services
Conclusion
Questions
7. Improved Social Business by Facebook
Introduction
Structure
Objective
Introduction to Facebook
Effects of Facebook on third-party business
Benefits of social media in businesses
Lead generation
Brand exposure and awareness
Targeted traffic
Market insights – research and competitor monitoring
Customer interaction – customer service and feedback
Cost-effective marketing techniques
Public relations and human resources
The current progress of FAIR for advancing socialmedia
Application of AI in the field at Facebook scale
Social media analytics
Potential use of DL in improving customers among social media users
Conclusion
Questions
8. Personalized Intelligent Computing by Apple
Structure
Objective
Introduction to Apple
Apple’s marketing strategy
Siri technology
AI in Apple: From Siri to the image processing
Apple uses DNN for face detection
How face ID detection system works
True depth camera system
Neural networks
Anti-spoofing mechanism in Face ID recognition
Other benefits of AI on smartphones
Innovation on intelligent product development
Emergence of Apple products year by year
Conclusion
Questions
9. Cloud Computing Intelligence by Microsoft
Introduction
Structure
Objective
Microsoft Approach to AI
Microsoft AI platform - Overview
Technical Stack of Microsoft AI platform
AI services
Cognitive services
Azure machine learning
Bot framework
AI infrastructure
Azure ML Studio
Azure ML Workbench
Visual Studio (VS) Code Tools for AI
Azure Notebooks
Deep learning framework
Incorporation of DL capabilities in cloud computing
Microsoft business model
Microsoft business segments
Conclusion
Questions
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