Python Scientific Computing

Anaconda is the first choice for beginner Python and entry machine learning. It is a Python distribution for scientific computing that provides package management and environment management capabilities to easily handle multi-version python coexistence, switching, and various third-party package installation issues.

C + + computer vision ccv-based on C language/provides cache/core machine Vision Library, novel Machine Vision Library opencv-It provides C + +, C, Python, Java and MATLAB interfaces, and supports Windows, Linux, Android and Mac OS operating system. General machine learning Mlpack dlib Ecogg Shark Closure Universal machine learning Closure Toolbox-cloj ...

Visit is an interactive and intuitive view of science http://www.aliyun.com/zixun/aggregation/12238.html "> Data visualization and Graphical analysis tools." Users can quickly generate their data visualization, animate, manipulate them, and save the images produced by the demo. Access includes a rich set of visual features that allow you to view data in a variety of ways. It can be used for both two-dimensional and three-dimensional (2d and 3D) structured and unstructured grid-defined scalar and vector field visualization ...

Translation: Esri Lucas The first paper on the Spark framework published by Matei, from the University of California, AMP Lab, is limited to my English proficiency, so there must be a lot of mistakes in translation, please find the wrong direct contact with me, thanks. (in parentheses, the italic part is my own interpretation) Summary: MapReduce and its various variants, conducted on a commercial cluster on a large scale ...

Introduction: This article explores the development of the Julia Language and its new features. The author thinks that the birth of a new language is bound to set off a new whirlwind, developers in the enjoyment of it to bring fun while also arguing for its existence value, whether Julia can bring new gospel to developers? Let's go into it together: Why create the Julia programming language? In a word, because we thirst for knowledge, constant pursuit. We have the core users of MATLAB, there are good at Lisp hackers, Pythonistas and Ru ...

Why create a Julia programming language? In a word, because we thirst for knowledge, constant pursuit. We have the core users of MATLAB, there are good at Lisp hackers, Pythonistas and rubyists experts also have a lot; In addition, there are some Perl Daniel, some developers used Mathematica before we had a little understanding of the fur. In other words, they understand more than just the fur, more than others, the development of R language. and C language for us is a deserted island. I ...

Introduction: It is well known that R is unparalleled in solving statistical problems. But R is slow at data speeds up to 2G, creating a solution that runs distributed algorithms in conjunction with Hadoop, but is there a team that uses solutions like python + Hadoop? R Such origins in the statistical computer package and Hadoop combination will not be a problem? The answer from the king of Frank: Because they do not understand the characteristics of R and Hadoop application scenarios, just ...

The computer came into my life very early. However, I have always wandered as a layman: neither a computer education nor an IT industry. Think of yourself these years in the front of the door dangling, leaving and gathering, quite sentimental. The first time close contact with primary school, the family put a Lenovo "Qin" computer. Its domineering side leaky speaker, incomparable pull the wind microphone, mysterious remote control, all shook my heart. Then the little boy would spend an afternoon studying the difference between the left and right keys and the function of the "Start" menu. In junior high School, the game became synonymous with computers. I'm buying Volkswagen software at the newsstand ...

Machine learning engineers are part of the team that develops products and builds algorithms and ensures that they work reliably, quickly, and on a scale.

Open source machine learning tools also allow you to migrate learning, which means you can solve machine learning problems by applying other aspects of knowledge.