books about AI
perspectives on the impact of AI on human society
- "Superintelligence: Paths, Dangers, Strategies" by Nick Bostrom
- "Life 3.0: Being Human in the Age of Artificial Intelligence" by Max Tegmark
- "The Age of Em: Work, Love, and Life when Robots Rule the Earth" by Robin Hanson
- "AI Superpowers: China, Silicon Valley, and the New World Order" by Kai-Fu Lee
- "The Singularity Is Near: When Humans Transcend Biology" by Ray Kurzweil
- "The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies" by Erik Brynjolfsson and Andrew McAfee
- "The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future" by Kevin Kelly
- "The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity" by Byron Reese
- "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World" by Pedro Domingos
- "Robot-Proof: Higher Education in the Age of Artificial Intelligence" by Joseph E. Aoun
books focused on gaining the knowledge necessary to create AI applications:
- 1. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
- 2. "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili
- 3. "Deep Learning for Beginners: Concepts and Techniques" by Mathieu Cliche
- 4. "Machine Learning Yearning" by Josh Gordon and Martin Görner
- 5. "Grokking Deep Learning" by Andrew Trask
- 6. "Deep Learning Illustrated" by Jon Krohn, Grant Beyleveld, and Aglaé Bassens
- 7. "Applied Machine Learning" by Kelleher, Mac Namee, and D'Arcy
- 8. "Introduction to Artificial Intelligence" by Wolfgang Ertel
- 9. "Artificial Intelligence: Foundations of Computational Agents" by Poole, Mackworth, and Goebel
- 10. "Practical Machine Learning for Computer Vision" by Martin Görner, Ryan Gillard, Valliappa Lakshmanan
books that offer diverse perspectives on AI, its challenges, and its impact on various aspects of society
- 1. "Artificial Intelligence: A Guide to Intelligent Systems" by Michael Negnevitsky
- 2. "Artificial Intelligence: Structures and Strategies for Complex Problem Solving" by George F. Luger
- 3. "Artificial Intelligence: A Systems Approach" by Michael Negnevitsky
- 4. "AI: A Very Short Introduction" by Margaret A. Boden
- 5. "Artificial Intelligence Basics: A Non-Technical Introduction" by Tom Taulli
- 6. "The Book of Why: The New Science of Cause and Effect" by Judea Pearl and Dana Mackenziey
- 7. "Human Compatible: Artificial Intelligence and the Problem of Control" by Stuart Russell
- 8. "Possible Minds: Twenty-Five Ways of Looking at AI" edited by John Brockman
- 9. "The Sentient Machine: The Coming Age of Artificial Intelligence" by Amir Husain
- 10. "Rebooting AI: Building Artificial Intelligence We Can Trust" by Gary Marcus and Ernest Davis
Books on AI
- Artificial Intelligence: A Guide to Intelligent Systems by Michael Negnevitsky
- Artificial Intelligence: Structures and Strategies for Complex Problem Solving by George F Luger
- Artificial Intelligence: A Systems Approach by Michael Negnevitsky
- AI: A Very Short Introduction by Margaret A Boden
- Artificial Intelligence Basics: A Non-Technical Introduction by Tom Taulli
- The Book of Why: The New Science of Cause and Effect by Judea Pearl and Dana Mackenzie
- Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell
- Possible Minds: Twenty-Five Ways of Looking at AI edited by John Brockman
- The Sentient Machine: The Coming Age of Artificial Intelligence by Amir Husain
- Rebooting AI: Building Artificial Intelligence We Can Trust by Gary Marcus and Ernest Davis
Learning How to Train AI
"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron:
This book provides a practical, hands-on approach to building machine learning models and neural networks using popular libraries like Scikit-Learn, Keras, and TensorFlow. It's a great starting point for understanding how to implement AI solutions for specific business problems.
"Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili:
This book covers the fundamentals of machine learning using Python and introduces various machine learning algorithms and techniques. It's valuable for learning the foundations of AI and how to apply them in a business context.
"Practical Natural Language Processing" by Sowmya Vajjala, Bodhisattwa Majumder, and Anuj Gupta:
If your business domain involves text data, this book is essential. It covers natural language processing (NLP) techniques and tools for processing and analyzing textual data, including text classification, sentiment analysis, and more.
"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville:
Deep learning is a crucial aspect of AI, particularly for tasks like image recognition and speech processing. This book is considered a comprehensive guide to deep learning, covering the theoretical foundations and practical implementations.
"Building Machine Learning Powered Applications" by Emmanuel Ameisen:
This book focuses on the practical aspects of building AI-powered applications for real-world business scenarios. It explores various case studies and provides insights into the process of deploying machine learning models in production.