The heavy release of Julia 1.0 must have disturbed the hearts of many developers-should I switch to Julia from Python or R? After all, the latter is a language that combines the advantages of Pyhton, R and C++++. No matter what you think, the goal of Julia's developers is to make Julia the most mainstream language for machine learning.
Is Julia, whose popularity is soaring, worthy of your possession?
Last week, the development team of the open source programming language—Julia released version 1.0 of the project. This shows that a programming language for optimized data analysis and deep learning has been born!
Why Julia?
For data scientists who are accustomed to mainstream languages ​​like R and Python, the birth of new languages ​​may cause some confusion for them. They may have spent several years learning their favorite programming language, and understand many of the minutiae knowledge points of the language, or they may have summarized some skills in using the language. In this case, they are probably already very comfortable with using stable and easy-to-use languages ​​like R and Python. So, why should they consider learning a new language now?
One of the founders of Julia, Viral Shah said: "If you are a mathematician, scientist or engineer, then you used to choose a programming language from the perspective of its speed, such as C++ or Java, or from the ease of language learning. Think about it from a perspective, such as Matlab, R, or Python. And we created a language that has both, called Julia."
Viral Shah said that Julia's development inspiration is to see how many people need to reprogram the same code. Data scientists will first use tools such as Python or R to develop algorithms, because it is easy to study data and make graphs in these languages. Then, when they feel that the algorithm is about to take shape, they will rewrite the program in C++ or Java to obtain faster computer processing performance. And Julia is faster than R and Python, because it is specifically used to quickly implement most of the basic mathematical knowledge or concepts required by data science, such as matrix expressions and linear algebra.
Julia has been widely used (more than 2 million downloads), but the user community is obviously not satisfied with this achievement. They hope that Julia can surpass Python and R as the central language of data science (especially machine learning).
Most of Julia's key technology development comes from MIT's Julia Lab, which is led by Julia colleague and MIT mathematics professor Alan Edelman. The other two creators of Julia are Jeff Bezanson and Stefan Karpinski. These developers' contribution to 1.0 development is a small part, and the most important thing comes from the efforts of more than 700 volunteers.
Why not Julia?
So, why not every data scientist needs to learn Julia? The reasons are as follows:
First, if processing speed is not important to you, Julia may not be as good as any product you are using (at least for now). The statistical work done by some R users is performed on relatively small data sets, and only involves simple calculations. In the R developer community, especially the data scientist Hadley Wickham, has developed a very good tool, through complete documentation, you can complete simple data analysis tasks. But if you use Julia to achieve some basic tasks completed in R, you can find that Julia's tools have no advantages for these tasks.
Second, Julia's tools for debugging and identifying performance issues lag behind Python and R. Shah said that Julia's basic development has been completed, and he hopes that the community can shift its focus to the development of these tools in order to make new users better and easier to use.
Julia is not a new product, in fact it has been around since 2012. But with the arrival of version 1.0, its popularity and attention have also risen sharply. Last week, analysts at the TIOBE programming language index pointed out that Julia entered the top 50 for the first time.
Dan Kopf from QUARTZ said: "Although many people are not ready to learn Julia, it is believed that if Julia's current shortcomings can be solved, its speed advantage will make it a data scientist. Mainstream programming language.†And RedMonk also said, “Julia is about to take over Python!â€
So the question is, will you choose to learn Julia?
pen usb stick customized,promotional pen usb flash drive,custom flash drive pens,ballpoint pen with usb flash drive,Pen with USB drive and laser pointer
Shenzhen Konchang Electronic Technology Co.,Ltd , https://www.konchangs.com