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Julia Programming - The Complete Reference - OneDDL - 12-18-2023
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Free Download Julia Programming - The Complete Reference
Published 12/2023
Created by Prof . Mathew K K Kanhirathinkal Kurian
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 33 Lectures ( 27h 0m ) | Size: 22.2 GB
Julia is a free and open source programming language. Julia code speed as good as C/C++. Statistical computing like R.
What you'll learn
This course teaches you the Programming language Julia, which is heralded as the language of Data Science & Machine Learning. Julia is best suited for it.
Julia is a free and open source programming language. Has the speed of C/C++. Similar to Ruby in dynamic typing. Like Mat lab in mathematical computations.
Works same as Python for general purpose programming languages. Similar in Statistical computing like R. Great in prototyping , performance and speed ..
Julia is JIT compiled. It is optionally typed. It has a nice mathematical syntax. It is general purpose too. Can be used for Web development , Game development.
Requirements
An aptitude for learning new technologies and techniques. Some sort of a scientific temperament . Above all a thirst for knowledge.
Description
Julia is a high-level, dynamic programming language, designed to give users the speed of C/C++ while remaining as easy to use as Python. This means that developers can solve problems faster and more effectively. Julia is great for computationally complex problems. Julia is a general-purpose language also and can be used for tasks like Web Development, Game Development, and more. Many view Julia as the next-generation language for Machine Learning and Data Science . It was developed mainly for numerical computation purpose, and it helps eliminate performance issues. It will provide an environment which is good enough to develop applications that require high performances. Julia is JIT Compiled. Write code that looks interpreted, and yet it gets to run as just as fast as compiled code. No need to vectorize code for performance, de vectorized code is fast. •Optionally Typed. Do some rapid prototyping with maximum flexibility, and then optimize for performance. Nice Mathematical Syntax. Builds upon and goes much further than classical mathematical languages like Fortran, Mat lab, and Mathematica. •General purpose. Get code from the package manager to perform all sort of tasks, from reading multiple types of databases, to data visualization, or running an HTTP server. •Similar to Ruby in Dynamic types.
Who this course is for
Julia is considered the language for Data Science and Machine Learning. Those who are doing Data Science and Machine Learning in Python can think of opting for Julia , because Julia is best for large database.
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