Python Natural Language

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Ceylon: real progress or just another language?

The road to computer science is littered with things that will become "the next big thing". Although many niche languages do find some place in scripts or specific applications, C (and its derivatives) and Java languages are hard to replace. But Red Hat's Ceylon seems to be an interesting combination of some language features, using the well-known C-style syntax, but it also provides object-oriented and some useful functional support in addition to simplicity. Take a look at Ceylon and see this future VM ...

Recommended! The machine learning resources compiled by foreign programmers

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 ...

Developing spark applications using Scala language

Developing spark applications with Scala language [goto: Dong's blog http://www.dongxicheng.org] Spark kernel is developed by Scala, so it is natural to develop spark applications using Scala.   If you are unfamiliar with the Scala language, you can read Web tutorials a Scala Tutorial for Java programmers or related Scala books to learn. This article will introduce ...

In-depth analysis of the five dimensions, the entry AI first needs to choose the direction

The pace of human use of machines to help production has never stopped. To enter the AI ??field, we must first understand the current structural system of the artificial intelligence industry.

The combination of Spark and Hadoop

Spark can read and write data directly to HDFS and also supports Spark on YARN. Spark runs in the same cluster as MapReduce, shares storage resources and calculations, borrows Hive from the data warehouse Shark implementation, and is almost completely compatible with Hive. Spark's core concepts 1, Resilient Distributed Dataset (RDD) flexible distribution data set RDD is ...

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15 major frameworks for machine learning

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.

11 Open Source machine learning project worth Mark

Spam filtering, face recognition, recommendation engine-when you have a large dataset and want to use them to perform predictive analysis and pattern recognition, machine learning is the only way. In this science, computers can learn, analyze and manipulate data independently without prior planning, and more and more developers are now concerned with machine learning. The rise of machine learning technology is also important not only because hardware costs are getting cheaper and more powerful, but free software surges that machine learning is easily deployed on stand-alone or large-scale clusters The diversity of machine learning libraries means that whatever language you like ...

Top 10 data mining tools most needed for big data

The following small series summarizes 10 best data mining tools for everyone, which can help you analyze big data from various angles and make correct business decisions through data.

Ali cloud ODPS vision, technology and difficulties

In January 2014, Aliyun opened up its ODPS service to open beta. In April 2014, all contestants of the Alibaba big data contest will commission and test the algorithm on the ODPS platform. In the same month, ODPS will also open more advanced functions into the open beta. InfoQ Chinese Station recently conducted an interview with Xu Changliang, the technical leader of the ODPS platform, and exchanged such topics as the vision, technology implementation and implementation difficulties of ODPS. InfoQ: Let's talk about the current situation of ODPS. What can this product do? Xu Changliang: ODPS is officially in 2011 ...

Learn Python for Machine learning algorithms

Machine learning uses algorithms to extract information from raw data and present it in some type of model. We use this model to infer other data that has not been modeled.

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