Kaggle Machine Learning

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 ...

Many companies are beginning to pay attention to large data and data analysis, but the talent is difficult to find Ah! In fact, it's easy to train a new programmer to be a capable data scientist, just a few cloud servers, and then learn a few weeks of machine learning on the Internet with a data specialist. The most famous data scientist training case is the "Enterprise Prediction Solution Platform" Kaggle, the latest award winner, Carter S. The Kaggle user developed a "overkill" using a simple but highly efficient approach ...

Many companies are beginning to pay attention to large data and data analysis, but the talent is difficult to find Ah! In fact, it's easy to train a new programmer to be a capable data scientist, just a few cloud servers, and then learn a few weeks of machine learning on the Internet with a data specialist. The most famous data scientist training case is the "Enterprise Prediction Solution Platform" Kaggle, the latest award winner, Carter S. The Kaggle user has developed a "overkill" analysis tool to advance the use of simple but efficient methods.

The large data in the wall are registered as dead data. Large data requires open innovation, from data openness, sharing and trading, to the opening of the value extraction ability, then to the foundation processing and analysis of the open platform, so that the data as the blood in the body of the data society long flow, moisture data economy, so that more long tail enterprises and data thinking innovators have a colorful chemical role, 　　To create a golden age of big data. My large data research trajectory I have been 4-5 years of mobile architecture and Java Virtual Machine, 4-5 years of nuclear architecture and parallel programming system, the last 4-5 years also in pursuit ...

In recent years, large numbers have become the driving force behind the growth of our digital life from popular words and concepts that are unique to big companies. 　　Here are five trends in the processing and development of large data in the future. 1. The increasing popularity of data science with the advent of web-based educational platforms such as Coursera, Udacity and edx, more and more people can learn all the knowledge, from basic statistical knowledge to natural language processing and machine learning, without spending a penny. Except this ...

In recent years, big data has changed from the popular words and concepts unique to large companies to the driving force behind the development of our digital life. The following are five trends in the processing and development of big data in the future.

Big data in 2012 was thoroughly at the end of a fire. From your corporate executives to U.S. President Barack Obama is talking about big data. But are these arty people talking about large numbers able to accurately define the data for big data? Indeed, even experts seem to have a very divergent definition. 2012 Large Data Industry list we spent a year with suppliers and some large data early adopters on the issue of large data. We interviewed the business is not a web-based enterprise, as well as LinkedIn, Eventbrite ...

It's no secret that data scientists are in short supply. Data explosions and corresponding explosion-proof tools, along with Moore's and Metcalfe's laws, have led to more data, links, and technology than ever before. In last year's Hadoop world, there was a frenzy of data scientists who barely met the needs of technology-oriented data architects. This means: 1. Potential MacArthur grant recipients, who need a passion and insight into data, math and statistics skills, it ...

The time for data scientist Shinton! But what exactly is a data scientist? What skills do data scientists need? Why are they different? The advent of the large data age has driven the growth of data, bandwidth, and processing power exponentially levels. Nowadays, the new field of data science has aroused great interest. "The data is crude, but oil needs to be refined before it can be used," says Amazon's former chief scientist. Today, organizations of all sizes are trying to explore how to dig out valuable information from large data. Data ...