Learning With Big Messy Data

Large data is a vague expression of large-scale phenomena. 　　The term has now been overheated by entrepreneurs, scientists, governments and the media. Five years ago, a Google research team announced a remarkable achievement in the world's top science magazine Nature. The team can track the spread of flu in the United States, which does not depend on any medical examination. They are tracking even faster than the CDC. Google's tracking results are only a day's delay, and the CDC needs to compile a large number of physicians ' diagnoses to get a spread of the trend chart ...

In this article, I want to share with you 8 neural network architectures. I believe that any machine learning researcher should be familiar with this process to promote their work.

In the past two years, the big data has been widely discussed by the public, and even become the selling point of many business marketing. Undoubtedly, the development and popularization of intelligent devices make the mass data collection possible. But the big data is not pure "data big", it contains a kind of calculation and thinking way change, want to exert big data insight, still face the challenge of collecting, managing, analyzing data. How can these obstacles be broken? How can large data be used in the future to create greater value? These questions are worth our cool judgment in the heat of the big data. April 26, Tsinghua University set up "Tsinghua ...

The big data is a vague and ambiguous term used to describe a large-scale phenomenon that is now rapidly becoming the focus of entrepreneurs, scientists, governments and the media. The big data is impressive. 5 years ago, a Google research team published a remarkable study in the world's most famous science journal Nature. Without any medical test results, the team was able to track the spread of flu trends across the United States at the time, and it was even faster than the US Centers for Disease Control (CDC). Google's tracking is only later than the outbreak of flu ...

Some tasks are more complicated to code directly. We can't handle all the nuances and simple coding. Therefore, machine learning is necessary. Instead, we provide a large amount of data to machine learning algorithms, allowing the algorithm to continuously explore the data and build models to solve the problem.

First of all, do not think that read this article can become a big data master, otherwise it will not use the word "cultivation", to cultivate into large data master is not an easy thing, can be said to be very difficult one thing. Or not even the big data source-the United States does not exceed 10 people (perhaps 5, 6) to reach this level, in China ... 　　Forget it, I won't say it. This article is actually for you to refer to a process unusually difficult, but the future is very bright road. No perseverance, look at the good, never seriously. (To be honest, even if you want to see this article ...)

This article originally contained the FT website, original title: Big Data:are We making a big mistake, it seems that I saw a bit late, but still share it. Because it does discuss some of the issues I've been thinking about recently, it's a speculative article. 　　If you've never known big data before, this can be considered a primer. This article is only opinion, because the query is more simple than the proof, but the big data is now hot, these negative comments, if as a conversation to collect, is also good ...

I'm a novice in data mining and machine learning, starting with Amazon last July, and because of the need for passive contact with work that has never been contacted before and what is needed to predict machine learning. Later, to Taobao, their own initiative to do a few months and user address related data mining work, there are some superficial experience. 　　Anyway, welcome advice and discussion. In addition, note that the title of this article imitates an American drama "Game of Thrones: The Song of Ice and Fire". In the world of data, we see a lot of cattle, very strong ...

Tags: Big Data Machine Learning Feature Engineering During that 11 years I just joined Baidu, at Phoenix Nest through machine learning to predict ad clicks. I was still very much surprised at the crazy growth in training data over the past two years. Everyone is passionate about the features, each new feature can immediately get the AUC increase and revenue growth. We firmly believe that the feature is benevolent, I believe there will be a steady stream of features to join, the data size will double the growth. I am also deeply infected, I firmly believe that the next two years the data will be at least ten times longer, so all the work around this fake ...

2013, the concept of "big data" into our vision, scholars in the introduction, the government officials talking about, the world's internet companies have started "Big data" competition, the media will be called this year "big data." 　　Large data, is from the technical hot word into a social wave and even national strategy. With the rapid development of Internet technology, large amount of information, a wide range of types, low value density, speed, and aging of large data attracted more and more attention, the great data brought about by the storm is changing our lives, work and thinking. Needless to be, public opinion service ...