Perfect for data scientists, students, and applied researchers, this book offers practical Python tutorials and simulations mirroring real-world case studies for mastering network machine learning. Essential for uncovering insights in social networks, ecological systems, brain connectivity, and many other domains.
Perfect for data scientists, students, and applied researchers, this book offers practical Python tutorials and simulations mirroring real-world case studies for mastering network machine learning. Essential for uncovering insights in social networks, ecological systems, brain connectivity, and many other domains.
Eric W. Bridgeford is a postdoctoral scholar in the Department of Psychology at Stanford University. Eric's background includes Computer Science, Bioengineering, and Biostatistics, and he develops methods for veridical data science. Eric is interested in biases presenting inferential obstacles to neuroscience, and how these limitations challenge analytical approaches and clinical adoption of neuroimaging methods.
Inhaltsangabe
Preface Terminology Part I. Foundations: 1. The network machine learning landscape 2. End-to-end biology network machine learning project Part II. Representations: 3. Characterizing and preparing network data 4. Statistical models of random networks 5. Learning network representations Part III. Applications: 6. Applications for a single network 7. Applications for two networks 8. Applications for multiple networks 9. Deep learning methods Appendix A. Network model theory Appendix B. Learning representations theory Appendix C. Overview of machine learning techniques Index.
Preface Terminology Part I. Foundations: 1. The network machine learning landscape 2. End-to-end biology network machine learning project Part II. Representations: 3. Characterizing and preparing network data 4. Statistical models of random networks 5. Learning network representations Part III. Applications: 6. Applications for a single network 7. Applications for two networks 8. Applications for multiple networks 9. Deep learning methods Appendix A. Network model theory Appendix B. Learning representations theory Appendix C. Overview of machine learning techniques Index.
Es gelten unsere Allgemeinen Geschäftsbedingungen: www.buecher.de/agb
Impressum
www.buecher.de ist ein Internetauftritt der buecher.de internetstores GmbH
Geschäftsführung: Monica Sawhney | Roland Kölbl | Günter Hilger
Sitz der Gesellschaft: Batheyer Straße 115 - 117, 58099 Hagen
Postanschrift: Bürgermeister-Wegele-Str. 12, 86167 Augsburg
Amtsgericht Hagen HRB 13257
Steuernummer: 321/5800/1497
USt-IdNr: DE450055826
