Julia For Machine Learning
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Statistics with Julia
This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics. The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book’s associated GitHub repository online. See what co-creators of the Julia language are saying about the book: Professor Alan Edelman, MIT: With “Statistics with Julia”, Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference. Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language. This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia.
Julia for Machine Learning
Unleash the power of Julia for your machine learning tasks. We reveal why Julia is chosen for more and more data science and machine learning projects, including Julia's ability to run algorithms at lightning speed. Next, we show you how to set up Julia and various IDEs such as Jupyter. Afterward, we explore key Julia libraries, which are useful for data science work, including packages related to visuals, data structures, and mathematical processes. After building a foundation in Julia, we dive into machine learning, with foundational concepts reinforced by Julia use cases. The use cases build upon each other, reaching the level where we code a machine learning model from scratch using Julia. All of these use cases are available in a series of Jupyter notebooks. After covering dimensionality reduction methods, we explore additional machine learning topics, such as parallelization and data engineering. Although knowing how to use Julia is essential, it is even more important to communicate our results to the business, which we cover next, including how to work efficiently with project stakeholders. Our Julia journey then ascends to the finer points, including improving machine learning transparency, reconciling machine learning with statistics, and continuing to innovate with Julia. The final chapters cover future trends in the areas of Julia, machine learning, and artificial intelligence. We explain machine learning and Bayesian Statistics hybrid systems, and Julia's Gen language. We share many resources so you can continue to sharpen your Julia and machine learning skills. Each chapter concludes with a series of questions designed to reinforce that chapter's material, with answers provided in an appendix. Other appendices include an extensive glossary, bridge packages between Julia and other programming languages, and an overview of three data science-related heuristics implemented in Julia, which aren't in any of the existing packages.
Data Science with Julia
"This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist."- Professor Charles Bouveyron, INRIA Chair in Data Science, Université Côte d’Azur, Nice, France Julia, an open-source programming language, was created to be as easy to use as languages such as R and Python while also as fast as C and Fortran. An accessible, intuitive, and highly efficient base language with speed that exceeds R and Python, makes Julia a formidable language for data science. Using well known data science methods that will motivate the reader, Data Science with Julia will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work. Features: Covers the core components of Julia as well as packages relevant to the input, manipulation and representation of data. Discusses several important topics in data science including supervised and unsupervised learning. Reviews data visualization using the Gadfly package, which was designed to emulate the very popular ggplot2 package in R. Readers will learn how to make many common plots and how to visualize model results. Presents how to optimize Julia code for performance. Will be an ideal source for people who already know R and want to learn how to use Julia (though no previous knowledge of R or any other programming language is required). The advantages of Julia for data science cannot be understated. Besides speed and ease of use, there are already over 1,900 packages available and Julia can interface (either directly or through packages) with libraries written in R, Python, Matlab, C, C++ or Fortran. The book is for senior undergraduates, beginning graduate students, or practicing data scientists who want to learn how to use Julia for data science. "This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist." Professor Charles Bouveyron INRIA Chair in Data Science Université Côte d’Azur, Nice, France