Raghvinder S. Sangwan,
Satish Mahadevan Srinivasan,
Satish Mahadevan Srinivasan
What Every Engineer Should Know About Artificial Intelligence and Big Data
Gebonden Engels 2026 1e druk 9781032829876Levertijd ongeveer 11 werkdagen
Gratis verzonden
Samenvatting
This book covers the essentials of big data and ML/AI to predict trends and risks for business while acknowledging that the field is extensive and evolving. Rather than focusing on theory, it shares real-life experiences building AI and big data analytics systems of value to practitioners.
Specificaties
ISBN13:9781032829876
Taal:Engels
Bindwijze:Gebonden
Aantal pagina's:304
Uitgever:CRC Press
Druk:1
Hoofdrubriek:Wetenschap en techniek, Computer en informatica
Thema:
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Inhoudsopgave
Part I Foundations and Platforms: Automation and Data Quality at Scale
Chapter 1 Fundamental Concepts in AI
Chapter 2 Big Data and Artificial Intelligence Systems
Chapter 3 Architecting Big Data Pipelines
Chapter 4 Big Data Frameworks and Data Cleaning Strategies
Chapter 5 Building Automated Pipelines for Data Cleaning
Part II Optimization and Search
Chapter 6 Swarm Intelligence
Chapter 7 Genetic Programming
Part III Learning Systems
Chapter 8 Foundations on Machine Learning and Artificial Learning
Chapter 9 Reinforcement Learning
Chapter 10 Deep Reinforcement Learning
Chapter 11 Natural Language Modeling
Chapter 12 Transformer Architecture and Evolution of LLMs
Part IV Systems in the Real World
Chapter 13 Architecting Distributed AI Systems Using Design Patterns
Chapter 14 Securing AI Systems
Chapter 15 AI System Safety in Practice
Chapter 16 Testing Strategies for AI Applications
Answer Keys for Chapter Questions
Chapter 1 Fundamental Concepts in AI
Chapter 2 Big Data and Artificial Intelligence Systems
Chapter 3 Architecting Big Data Pipelines
Chapter 4 Big Data Frameworks and Data Cleaning Strategies
Chapter 5 Building Automated Pipelines for Data Cleaning
Part II Optimization and Search
Chapter 6 Swarm Intelligence
Chapter 7 Genetic Programming
Part III Learning Systems
Chapter 8 Foundations on Machine Learning and Artificial Learning
Chapter 9 Reinforcement Learning
Chapter 10 Deep Reinforcement Learning
Chapter 11 Natural Language Modeling
Chapter 12 Transformer Architecture and Evolution of LLMs
Part IV Systems in the Real World
Chapter 13 Architecting Distributed AI Systems Using Design Patterns
Chapter 14 Securing AI Systems
Chapter 15 AI System Safety in Practice
Chapter 16 Testing Strategies for AI Applications
Answer Keys for Chapter Questions
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