Bezanson, J., Edelman, A., Karpinski, S. & Shah, V. B. (2017). Julia: A Fresh Approach to Numerical Computing. SIAM review, 59(1), 65–98.
Blaom, A. D., Kiraly, F., Lienart, T., Simillides, Y., Arenas, D. & Vollmer, S. J. (2020). MLJ: A Julia Package for Composable Machine Learning. Journal of Open Source Software, 5(55), 2704. https://doi.org/10.21105/joss.02704
Breloff, T., Schwabeneder, D., Borregaard, M. K., Christ, S., Heinen, J., Yuval, Palugniok, A.,Simon, Vertechi, P., Zhanibek, Chamberlin, T., ma-laforge, Rackauckas, C., Schulz,O., Pfitzner, S., Arakaki, T., Yahyaabadi, A., Devine, J., Pech, S., ... Watson, S. S.(2021). JuliaPlots/Plots.Jl: V1.13.2. https://doi.org/10.5281/zenodo.4725318
Bromberger, S. & Contributors, O. (2017). Juliagraphs/Lightgraphs.Jl: An Optimized Graphs Package for the Julia Programming Language. https://doi.org/10.5281/ZENODO.889971
Chen, J. & Revels, J. (2016). Robust Benchmarking in Noisy Environments. arXiv:1608.04295[cs].
Dunning, I., Huchette, J. & Lubin, M. (2017). JuMP: A Modeling Language for Mathematical Optimization. SIAM Review, 59(2), 295–320. https://doi.org/10.1137/15M1020575
Gao, K., Mei, G., Piccialli, F., Cuomo, S., Tu, J. & Huo, Z. (2020). Julia Language in Machine Learning: Algorithms, Applications, and Open Issues. Computer Science Review, 37,100254. https://doi.org/10.1016/j.cosrev.2020.100254
Ge, H., Xu, K. & Ghahramani, Z. (2018). Turing: A Language for Flexible Probabilistic Inference. International Conference on Artificial Intelligence and Statistics, 1682–1690.
Innes, M., Saba, E., Fischer, K., Gandhi, D., Rudilosso, M. C., Joy, N. M., Karmali, T.,Pal, A. & Shah, V. (2018). Fashionable Modelling with Flux. CoRR,abs/1811.01457. https://arxiv.org/abs/1811.01457
Laurie, H. (2021). Julia Programming for Nervous Beginners. https://juliaacademy.com/p/julia-programming-for-nervous-beginner
Innes, M. (2018). Flux: Elegant Machine Learning with Julia. Journal of Open Source Software. https://doi.org/10.21105/joss.00602
Lauwens, B. & Downey, A. B. (2019). Think Julia: How to Think Like a Computer Scientist (1st edition). O’Reilly Media.
Nazarathy, Y., & Klok, H. (2021). Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. Springer Nature.
Novak, K. (2021). Numerical Methods for Scientific Computing. 2nd Edition. Equal Share Press.
Perkel, J. M. (2019). Julia: Come for the Syntax, Stay for the Speed. Nature, 572(7767), 141–142. https://doi.org/10.1038/d41586-019-02310-3
Sengupta, A. & Edelman, A. (2019). Julia High Performance: Optimizations, Distributed Computing, Multithreading, and GPU Programming with Julia 1.0 and beyond, 2nd Edition. Packt Publishing.
Storopoli, J. (2021). Bayesian Statistics with Julia and Turing. https://storopoli.github.io/Bayesian-Julia
Storopoli, J., Huijzer, R., & Alonso L. (2021). Julia Data Science. https://juliadatascience.io
TEDx Talks. (2020). A Programming Language to Heal the Planet Together: Julia | AlanEdelman | TEDxMIT.
The Julia Programming Language. (2019). JuliaCon 2019 | The Unreasonable Effectivenessof Multiple Dispatch | Stefan Karpinski.
White, J. M., Kamiński, B., powerdistribution, Milan Bouchet-Valat, Garborg, S., Quinn,J., Kornblith, S., cjprybol, Stukalov, A., Bates, D., Short, T., DuBois, C., Harris,H., Squire, K., Arslan, A., pdeffebach, Anthoff, D., Kleinschmidt, D., Noack, A.,... White, L. (2020). JuliaData/DataFrames.Jl: V0.22.1. https://doi.org/10.5281/zenodo.4282946
Xu, K., Ge, H., Tebbutt, W., Tarek, M., Trapp, M. & Ghahramani, Z. (2020). AdvancedHMC.Jl: A Robust, Modular and Efficient Implementation of Advanced HMC Algorithms. Symposium on Advances in Approximate Bayesian Inference, 1–10.
