 |
5 books on AI for Software development [PDF]
October 27, 2024 | 20 |
These books explore automated code generation, AI-based bug detection, optimization, and other AI-enhanced development tools, illuminating how AI can transform the software development process.
1. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models
2024 by Sharma, Avinash Kumar, Chanderwal, Nitin, Prajapati, Amarjeet, Singh, Pancham, Kansal, Mrignainy
"Advancing Software Engineering Through AI, Federated Learning and Large Language Models" addresses the challenges of modern software systems' complexity and scale by exploring innovative approaches. Traditional methods struggle with efficiency, reliability and scalability, often leading to delays and increased costs due to manual, error-prone processes. This book offers a comprehensive examination of how AI, ML, Federated Learning and Large Language Models intersect with software engineering, providing real-world case studies, practical examples and implementation guidelines. It is an essential resource for researchers, academicians, practitioners, industrialists and students, offering interdisciplinary insights from experts in AI, ML, software engineering and ethics, ensuring readers can effectively apply these advanced concepts in their projects.
Download PDF
2. Optimising the Software Development Process with Artificial Intelligence
2023 by José Raúl Romero, Inmaculada Medina-Bulo, Francisco Chicano
This book serves as a pragmatic guide to applying artificial intelligence (AI) techniques for enhancing and streamlining various stages of the software development process, spanning from initial project planning to the latest deployment strategies. Authored by leading experts in the field, each chapter offers practical and reproducible examples. The book begins with an introductory chapter, followed by Chapters 2 through 9, which respectively employ AI techniques in the traditional phases of software development, including project management, requirement engineering, analysis and design, coding, cloud deployment, unit and system testing and maintenance. Chapters 10 and 11 then provide foundational tutorials on the AI methodologies featured in the preceding chapters, covering metaheuristics and machine learning. With its broad coverage and specific focus, this book represents an invaluable reference for researchers, professionals and students possessing a foundational understanding of software engineering.
Download PDF
3. AI and Machine Learning for Coders
2020 by Laurence Moroney
For programmers seeking to transition into AI specialists, "AI and Machine Learning for Coders" offers an ideal starting point. Drawing from Laurence Moroney's highly successful AI courses, this introductory book adopts a hands-on, code-centric approach designed to bolster your confidence as you delve into essential concepts. You'll gain proficiency in implementing prevalent machine learning scenarios, encompassing computer vision, natural language processing (NLP) and sequence modeling for diverse platforms, including web, mobile, cloud and embedded runtimes. Unlike many machine learning books that initially inundate readers with complex mathematics, this guide prioritizes practical lessons, enabling you to work directly with code. You'll acquire skills such as TensorFlow model construction, the fundamentals of machine learning through code examples, computer vision techniques, NLP for word and sentence processing, integration of models into Android and iOS platforms and serving models via web and cloud environments using TensorFlow Serving.
Download PDF
4. The Creativity Code: Art and Innovation in the Age of AI
2020 by Marcus Du Sautoy
In "The Creativity Code," the brilliant polymath Marcus du Sautoy delves into the realm of artificial intelligence and algorithmic learning, offering a vital roadmap to the future of creativity. Within these pages, du Sautoy examines the significance of patterns and imitation in the creative process, embarking on an exploration of the ingenious programs and visionary programmers—ranging from Deep Mind and the Flow Machine to Botnik and WHIM—who are actively striving to rival or surpass human ingenuity across domains like gaming, music, art and language. This enthralling journey through the landscape of invention unravels the new facets of creativity and unravels the enigmatic codes of human innovation.
Download PDF
5. Advances in Machine Learning Applications in Software Engineering
2006 by Zhang, Du, Tsai, Jeffery J.P.
The study of machine learning focuses on creating computer programs that enhance their performance through experience. In response to the challenges posed by the development and maintenance of larger and more complex software systems within dynamic and ever-changing environments, machine learning techniques have gained increasing importance in various software development and maintenance tasks. "Advances in Machine Learning Applications in Software Engineering" delves into the analysis, characterization and refinement of software engineering data utilizing machine learning methods. This book showcases the application of various machine learning approaches in the development and deployment of software systems, along with the utilization of these methods to construct predictive models for software quality. It provides valuable insights and sets the course for future research endeavors in this emerging field of study.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models
2024 by Sharma, Avinash Kumar, Chanderwal, Nitin, Prajapati, Amarjeet, Singh, Pancham, Kansal, Mrignainy
"Advancing Software Engineering Through AI, Federated Learning and Large Language Models" addresses the challenges of modern software systems' complexity and scale by exploring innovative approaches. Traditional methods struggle with efficiency, reliability and scalability, often leading to delays and increased costs due to manual, error-prone processes. This book offers a comprehensive examination of how AI, ML, Federated Learning and Large Language Models intersect with software engineering, providing real-world case studies, practical examples and implementation guidelines. It is an essential resource for researchers, academicians, practitioners, industrialists and students, offering interdisciplinary insights from experts in AI, ML, software engineering and ethics, ensuring readers can effectively apply these advanced concepts in their projects.
Download PDF
2. Optimising the Software Development Process with Artificial Intelligence
2023 by José Raúl Romero, Inmaculada Medina-Bulo, Francisco Chicano
This book serves as a pragmatic guide to applying artificial intelligence (AI) techniques for enhancing and streamlining various stages of the software development process, spanning from initial project planning to the latest deployment strategies. Authored by leading experts in the field, each chapter offers practical and reproducible examples. The book begins with an introductory chapter, followed by Chapters 2 through 9, which respectively employ AI techniques in the traditional phases of software development, including project management, requirement engineering, analysis and design, coding, cloud deployment, unit and system testing and maintenance. Chapters 10 and 11 then provide foundational tutorials on the AI methodologies featured in the preceding chapters, covering metaheuristics and machine learning. With its broad coverage and specific focus, this book represents an invaluable reference for researchers, professionals and students possessing a foundational understanding of software engineering.
Download PDF
3. AI and Machine Learning for Coders
2020 by Laurence Moroney
For programmers seeking to transition into AI specialists, "AI and Machine Learning for Coders" offers an ideal starting point. Drawing from Laurence Moroney's highly successful AI courses, this introductory book adopts a hands-on, code-centric approach designed to bolster your confidence as you delve into essential concepts. You'll gain proficiency in implementing prevalent machine learning scenarios, encompassing computer vision, natural language processing (NLP) and sequence modeling for diverse platforms, including web, mobile, cloud and embedded runtimes. Unlike many machine learning books that initially inundate readers with complex mathematics, this guide prioritizes practical lessons, enabling you to work directly with code. You'll acquire skills such as TensorFlow model construction, the fundamentals of machine learning through code examples, computer vision techniques, NLP for word and sentence processing, integration of models into Android and iOS platforms and serving models via web and cloud environments using TensorFlow Serving.
Download PDF
4. The Creativity Code: Art and Innovation in the Age of AI
2020 by Marcus Du Sautoy
In "The Creativity Code," the brilliant polymath Marcus du Sautoy delves into the realm of artificial intelligence and algorithmic learning, offering a vital roadmap to the future of creativity. Within these pages, du Sautoy examines the significance of patterns and imitation in the creative process, embarking on an exploration of the ingenious programs and visionary programmers—ranging from Deep Mind and the Flow Machine to Botnik and WHIM—who are actively striving to rival or surpass human ingenuity across domains like gaming, music, art and language. This enthralling journey through the landscape of invention unravels the new facets of creativity and unravels the enigmatic codes of human innovation.
Download PDF
5. Advances in Machine Learning Applications in Software Engineering
2006 by Zhang, Du, Tsai, Jeffery J.P.
The study of machine learning focuses on creating computer programs that enhance their performance through experience. In response to the challenges posed by the development and maintenance of larger and more complex software systems within dynamic and ever-changing environments, machine learning techniques have gained increasing importance in various software development and maintenance tasks. "Advances in Machine Learning Applications in Software Engineering" delves into the analysis, characterization and refinement of software engineering data utilizing machine learning methods. This book showcases the application of various machine learning approaches in the development and deployment of software systems, along with the utilization of these methods to construct predictive models for software quality. It provides valuable insights and sets the course for future research endeavors in this emerging field of study.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
