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Julia provides an extensive library of mathematical functions with great numerical accuracy. It's designed for distributed and parallel computation. Julia's efficient and cross-platform I/O is provided by the Node.js's libuv.įeatures and advantages of Julia can be summarized as follows: Most of Julia's core is implemented in C/C++. The development team of Julia aims to create a remarkable and never done before combination of power and efficiency without compromising the ease of use in one single language.
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Although Julia is really good at it, it is not restricted to just scientific computing as it can also be used for web and general purpose programming. These have been around for quite some time and Julia is highly influenced by them, especially when it comes to numerical and scientific computing. It is quite often compared with Python, R, MATLAB, and Octave. (Did we mention it should be as fast as C?) We want it interactive and we want it compiled. Something that is dirt simple to learn, yet keeps the most serious hackers happy. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. We want a language that's homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want the speed of C with the dynamism of Ruby. We want a language that's open source, with a liberal license. This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia. You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations. This has the practical coverage of statistics and machine learning. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining.
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This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. It is a good tool for a data science practitioner. Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete.