Summarize the democratization of machine learning from data, computing power, algorithms, and teaching

introduction

Machine learning used to be a technology that could only be used by PhDs and wealthy institutions. But today, there are too many tools for anyone to start learning machine learning. So stop making excuses!

This article will summarize how the four cornerstones of machine learning have been democratized in recent years.

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Summarize the democratization of machine learning from data, computing power, algorithms, and teaching

The four cornerstones of machine learning are data, computing power, algorithms, and teaching.

data

There is a list of public data sets on GitHub: awesomedata/awesome-public-datasets, which contains more than 500 public data sets. In addition, there are also a large number of collated data sets on Kaggle. Of course, you can also use scrapy to grab data from the Internet. Today, everyone has access to high-quality data sets.

Of course, technology giants have their own undisclosed data sets, but distributed startups like OpenMined are working to create services that allow data scientists to train their own models on those data.

Computing power

Is there a GPU? Machine learning, especially deep learning, requires a lot of expensive operations. Neural networks require massively parallel operations that GPUs are good at.

Unfortunately, GPUs are expensive. Fortunately, tools such as Google's CoLab and Kaggle's Kernel provide free (Tesla K80) GPUs.

algorithm

Algorithms are already a daily necessities. Fortunately, the culture of machine learning is open source and sharing results. Whether in NIPS or ICLR, most of the researchers are very happy to share their results.

If you want to track the latest research progress, Arxiv Sanity Preserver is highly recommended, where you can browse the latest papers. Of course, machine learning also has a special area r/MachineLearning on Reddit. You can use off-the-shelf code or use the free Tensorflow library to create your own model.

teaching

The stronger the ability, the greater the responsibility. With code, data, and computing power, you need to learn how to use them!

In addition to my own YouTube channel c/sirajraval, there are many free teaching resources online that can help you learn how to use machine learning tools. I made a 3-month course plan to help newbies get started: https://?v=Cr6VqTRO1v0

Summarize the democratization of machine learning from data, computing power, algorithms, and teaching

Go forward courageously

Are you very excited now? This is a wonderful time! Too many changes are taking place! Machine learning can help us understand the world in an unprecedented way, and can help us create and discover new things with unprecedented efficiency. You have gained power, use it wisely.

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