Artificial Intelligence and Deep Learning for Decision Makers: A Growth Hacker’s Guide to Cutting Edge Technologies
- Length: 248 pages
- Edition: 1
- Language: English
- Publisher: BPB Publications
- Publication Date: 2019-12-27
- ISBN-10: 9389328683
- ISBN-13: 9789389328684
- Sales Rank: #4377195 (See Top 100 Books)
Learn modern-day technologies from modern-day technical giants
Key Features
- Real-world success and failure stories of artificial intelligence explained
- Understand concepts of artificial intelligence and deep learning methods
- Learn how to use artificial intelligence and deep learning methods
- Know how to prepare dataset and implement models using industry leading Python packages
- You’ll be able to apply and analyze the results produced by the models for prediction
Description
The aim of this book is to help the readers understand the concept of artificial intelligence and deep learning methods and implement them into their businesses and organizations. The first two chapters describe the introduction of the artificial intelligence and deep learning methods. In the first chapter, the concept of human thinking process, starting from the biochemical responses within the structure of neurons to the problem-solving steps through computational thinking skills are discussed.
All chapters after the first two should be considered as the study of different technological and Artificial Intelligence giants of current age. These chapters are placed in a way that each chapter could be considered a separate study of a separate company, which includes the achievements of intelligent services currently provided by the company, discussion on the business model of the company towards the use of the deep learning technologies, the advancement of the web services which are incorporated with intelligent capability introduced by company, the efforts of the company in contributing to the development of the artificial intelligence and deep learning research.
What will you learn
How to use the algorithms written in the Python programming language to design models and perform predictions in general datasetsUnderstand use cases in different industries related to the implementation of artificial intelligence and deep learning methodsLearn the use of potential ideas in artificial intelligence and deep learning methods to improve the operational processes or new products and how services can be produced based on the methods
Who this book is for
This book is targeted to business and organization leaders, technology enthusiasts, professionals, and managers who seek knowledge of artificial intelligence and deep learning methods.
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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