Overview of this book

Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia. Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia. By the end of the book, you will have acquired the skills to work more effectively with your data
Title Page
Dedication
Contributors
Preface
Variables, Types, and Functions
Metaprogramming and Advanced Typing
Julia Workflow
Distributed Computing
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Index

Generating full factorial designs

Often in scientific computing, we are interested in generating a full factorial design of a computational experiment (see, for example, http://www.socialresearchmethods.net/kb/expfact.php or https://en.wikipedia.org/wiki/Factorial_experiment). A typical application of this design is performing a grid search in hyperparameter tuning of machine learning models (see https://cloud.google.com/ml-engine/docs/tensorflow/hyperparameter-tuning-overview or https://en.wikipedia.org/wiki/Hyperparameter_optimization#Grid_search).

Assume that we are given a list of vectors and we want to generate all possible combinations of values taken from those vectors. For instance, if we have the x=[1,2] and y=['a', 'b']vectors, we have four possible combinations of values taken from them, namely, (1,'a'), (2, 'a'), (1,'b'), and (2,'b'). In general, if we have

vectors, and vector

has

elements, then there are

such combinations. In this recipe, we will show how to use the matrix...