Hands-On Explainable AI (XAI) with Python: Interpret,visualize,explain,and integrate reliable AI for fair,secure,and trustworthy AI apps
by: Denis Rothman
Print Length 页数: 454 pages
ISBN-10: 1800208138
ISBN-13: 9781800208131
05 x 2.62 x 23.5 cm
Publisher finelybook 出版社: Packt Publishing (31 July 2020)
Language 语言: English
Book Description
Resolve the black box models in your AI applications to make them fair,trustworthy,and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces.
Effectively translating AI insights to business stakeholders requires careful planning,design,and visualization choices. Describing the problem,the model,and the relationships among variables and their findings are often subtle,surprising,and technically complex.
Hands-On Explainable AI (XAI) with Python will enable you to work with specific hands-on machine learning Python projects strategically arranged to enhance your grip on AI results analysis. The analysis includes building models,interpreting results with visualizations,and integrating understandable AI reporting tools and different applications.
You will build XAI solutions in Python,TensorFlow 2,Google Cloud’s XAI platform,Google Colaboratory,and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source explainable AI tools for Python that can be used throughout the machine learning project life-cycle.
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 learn
Plan 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 problems
