Kumble G.P. Practical Artificial Intelligence and Blockchain

Packt, 2020. — 268 p. — ISBN: 9781838822293.Build powerful artificial intelligent applications using Hyperledger, Ethereum, Corda, Chain Core, Quorum, IOTA and moreKey FeaturesDevelop your skills by understanding the fundamentals of the implementation of AI in blockchain
Get well-versed with AI-Blockchain ecosystem to build your smart Dapps
Leverage insights from multiple live use-cases of AI and Blockchain for your solutions using Ethereum and HyperledgerBook DescriptionBlockchain provides a distributed database of immutable records which are executed and shared among stakeholders. This guide will give you a headstart on the convergence of artificial intelligence and blockchain.We will start by explaining all the required blockchain concepts, variants along with their key differences and more. Further familiarising you with different types of Hyperledger framework such as Iroha, Fabric, Sawtooth, Indy, and Burrow. You will then understand key concepts of artificial neural networks, machine learning, deep learning with real-world examples.Moving ahead, you will see how the global decentralized databases, as well as file systems, will help you perform better AI analytics and how you can empower blockchain using AI algorithms and models. You will implement all required important scenarios to learn how to build cognitive decentralized applications. Later you will go through different domains and cover relevant case studies which will help us to link AI with blockchain technology. Lastly, you will learn how to develop, compile, and deploy your own smart contracts using the best techniques.By the end of this book, you will be able to build smart solutions AI systems with the clear convergence of AI and blockchain attributes in it.What you will learnUnderstand the fundamentals of Blockchain and AI methodologies using various technologies and frameworks such as Hyperledger and Ethereum
Learn the significance of data collection, cleaning, and processing in AI modeling
Understand the application of analytics in cryptocurrency with live use cases
Get to grips with open and private intelligent blockchains on the Ethereum platform
Understand the design approach behind adding intelligence factor to dApps (diApps)
Apply predictive analytics, sentiment analysis, ARIMA, forecasting, and so on in the cryptocurrencyWho This Book Is ForThis book is for blockchain and AI architects, developers, data scientists, data engineers and evangelists who want to bring the power of artificial intelligence in Blockchain applications. If you want a perfect blend of theoretical and practical use cases to understand how to implement smart cognitive insights into blockchain solutions, this book is what you need! Having some knowledge of machine learning and blockchain concepts are mandatory.You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting machine learning model visualizations into user explainable interfaces.By the end of this artificial intelligence book, you will possess an in-depth understanding of the core concepts of explainable AI.What you will learnPlan for explainable AI through the different stages of the machine learning life-cycle
Estimate the strengths and weaknesses of popular open-source explainable AI applications
Examine how to detect and handle bias issues in machine learning data
Review ethics considerations and tools to address common problems in machine learning data
Share explainable AI design and visualization best practices
Integrate explainable AI results using Python models
Use explainable AI toolkits for Python in machine learning life-cycles to solve business problemsWho This Book Is ForThis book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book.Some of the potential readers of this book include:Professionals who already use Python for as data science, machine learning, research, and analysis
Data analysts and data scientists who want an introduction into explainable AI tools and techniques
AI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications