Machine Learning with Go - The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios.
Peer reviewed journal articles
In preparation or submitted
- J. A. Novella, P. Emami, D. L. Whitenack, M. Capuccini, K. Kultima, O. Spjuth, Container-based bioinformatics with Pachyderm, in preparation for OUP Bioinformatics.
- D. L. Whitenack, Gophernotes: the Go kernel for Jupyter and nteract, in preparation for the Journal of Open Source Software.
- A.H. Larsen, U. De Giovannini, D.L. Whitenack, A. Wasserman, and A. Rubio, Stark Ionization of Atoms and Molecules within Density Functional Resonance Theory, J. Phys. Chem. Lett. 4, 2734 (2013). [link]
- A. J. Olson, D. L. Whitenack, Y. P. Chen, Effects of magnetic dipole-dipole interactions in atomic Bose-Einstein condensates with tunable s-wave interactions, Phys. Rev. A 88, 043609 (2013). [link]
- D. L. Whitenack, Linear response of uniformly complex-scaled many-body systems, Annalen der Physik 524, 814 (2012). [link]
- M. Mack, D. L. Whitenack and A. Wasserman, The influence of exchange-correlation asymptotics on the structure of high harmonic spectra, Chem. Phys. Lett. 558, 15 (2012). [link]
- D. L. Whitenack and A. Wasserman, Density functional resonance theory: Practical considerations, the complex density function, orbital energies, and functionals, J. Chem. Phys. 136, 164106 (2012). [link]
- D. L. Whitenack, Y. Zhang and A. Wasserman, Density functional derivative discontinuities at the maximum number of bound electrons, Phys. Rev. A. 85, 042504 (2012). [link]
- D. L. Whitenack and A. Wasserman, Density Functional Theory of Unbound Electronic Systems, Phys. Rev. Lett. 107, 163002 (2011). [link]
- D. L. Whitenack and A. Wasserman, Resonance Lifetimes from Complex Densities, J. Phys. Chem. Lett. 1, 407 (2010). [link]
Other recent publications
- D. L. Whitenack, Putting the Science Back in Data Science, O'Reilly Publishing (2017).
- D. L. Whitenack, From edge2cat to edge2anything with TensorFlow, YCombinator (2017).
- D. L. Whitenack, Distributed Deep Learning with Docker, Intel® Nervana™ technology, neon™, and Pachyderm, Intel Developer Zone (2017).
- D. L. Whitenack, Go for Big Data, Intel Developer Zone (2017).
- D. L. Whitenack, Introduction to Machine Learning for Go Programmers, O'Reilly Publishing (2017).