An easy-to-read journey through unconventional machine learning
Explore the present and future of machine learning through the lens of quantum and neuromorphic computing.
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About the co-authors
Iraitz Montalbán
Javier Mancilla
Tomás Tagliani
Quantum Computing and Machine Learning specialist with more than 20 years of experience. Ph.D. in Quantum Computing and Master in Data Management. Certified in quantum technologies by BIMTECH, MIT xPro, KAIST, IBM, and Saint Petersburg University. Javier specializes in the application of quantum machine learning in the finance sector. Co-author of "Financial Modeling using Quantum Computing" (Packt). Three consecutive years selected as Linkedin Quantum Top Voice.
Data Management and Advanced Analytics specialist. M.Sc. in Mathematical Modelling, M.Sc. in Quantum Computing Technologies and M.L. in Data Protection, also holds certificates from Scrum Alliance, MIT Sloan and IBM related to data and technology management. Iraitz specializes in Corporate Innovation and Strategic adoption of Disruptive Technologies for medium and large size enterprises. Co-author of "Financial Modeling using Quantum Computing" (Packt).
Data Science and Machine Learning Specialist. MBA and background in actuarial sciences. With a vast experience through multiple companies, across different countries and sizes, Tomas specializes in the conception, development and deployment of machine learning solutions, with a strong focus on solving business problems, specially in the financial sector.
"Unconventional Machine Learning" is perfect for data scientists, tech leaders, and startup founders eager to explore the latest in quantum and neuromorphic computing. This book offers deep insights and practical guidance on integrating these cutting-edge technologies to revolutionize machine learning. The objective is to stay ahead with actionable strategies and real-world applications.
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Book content
Chapter 1: Introduction to Unconventional Computing
Chapter 2: Introduction to Quantum Computing
Chapter 3: Introduction to Neuromorphic Computing
Chapter 4: Quantum Machine Learning
Chapter 5: Neuromorphic-Inspired Machine Learning
Chapter 6: Hardware Innovations in Quantum and Neuromorphic Technologies
Chapter 7: Real-World Applications of Quantum and Neuromorphic-Inspired Machine Learning
Chapter 8: Integration Strategies for Quantum and Neuromorphic Technologies
Chapter 9: Future Trends and Directions in Quantum and Neuromorphic-Inspired Machine Learning
Chapter 2: Introduction to Quantum Computing
Chapter 3: Introduction to Neuromorphic Computing
Chapter 4: Quantum Machine Learning
Chapter 5: Neuromorphic-Inspired Machine Learning
Chapter 6: Hardware Innovations in Quantum and Neuromorphic Technologies
Chapter 7: Real-World Applications of Quantum and Neuromorphic-Inspired Machine Learning
Chapter 8: Integration Strategies for Quantum and Neuromorphic Technologies
Chapter 9: Future Trends and Directions in Quantum and Neuromorphic-Inspired Machine Learning
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