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Julia in Action teaches you how to use the Julia language to tackle technical programming tasks as well as data processing, analysis, and visualization challenges. You'll begin by setting up your development environment and then you'll dive into the fundamentals: data types, strings, collections, matrices, and control flow. Using real-world examples, you'll learn how to create and use functions and methods along with how and when to use explicit types. You'll master reading and writing files and data, connecting to external data sources, and importing and cleaning real world data. This book also teaches you about parallel programming to help you split complex problems into distributable chunks for faster processing. By the end of this book, you'll be ready to use Julia to solve your scientific programming tasks.
9.5. Higher order functions and functions acting on functions
9.5.1. MapReduce: a practical example of higher order functions
9.5.2. Function composition
9.6. Putting it all together: writing MapReduce in Julia
9.6.1. Obtaining the data set
9.6.2. The Map stage
9.6.3. The Reduce stage
9.6.4. Concluding remarks
10. Types, conversion and promotion
11. I/O and file system management
12. Parallel programming
13. Metaprogramming, reflection and introspection
14. Packages, modules and package management
15. Epilogue: Onto bigger and better things
Appendix A: Testing and profiling
Appendix B: Invoking non-Julia code
About the Technology
Julia is a newcomer to the technical and scientific programming community currently dominated by R, Matlab, and Python. It was created with lofty goals: a general purpose, easy-to-learn open-source language that's blazingly fast and powerful and built with parallel computing and data science tasks in mind. Although Julia is a new language, its roots are in Lisp, so it comes with mature features like macros and support for other metaprogramming techniques like code generation. Julia's expressive grammar lets you write easy-to-read and easier-to-debug code, and its speed gets you through more work in less time. It's a great choice whether you're designing a machine learning system, crunching statistical data, or writing system utilities.
Julia from the ground up
Types, methods, and multiple dispatch
Parallel programming and efficient code
Metaprogramming and macros
About the reader
This book is suitable for data scientists to general purpose developers to engineers. You should have some programming experience. No specialized mathematical or data science experience is assumed.
Chris von Csefalvay uses Julia in his position as a data scientist specializing in machine learning. He's an author, contributor, and committer on more than fifty open-source projects.