Advice on building a machine learning career and reading research papers by Prof. Andrew Ng

By Mohamed Ali Habib, Computer Science Graduate   Since you’re reading this blog, you probably already know who is Andrew Ng, one of the pioneers in the field, and you maybe interested in his advice on how to build a career in Machine Learning.

This blog summarizes the career advice/reading research papers lecture in the CS230 Deep learning course by Stanford University on YouTube.

I would recommend watching the lecture for more details, it’s pretty informative.

However, I thought that you might find this helpful either you watch it or not.

Therefore, I tried to outline those recommendations here.

TL;DR: Skip to the key takeaways section.

Andrew shared two major recommendations, specifically:   How can you learn efficiently and relatively quickly through reading research papers.

So, what you should do in case you want to learn from the academic literature whether you want to learn to build a machine learning system/project of interest, or just to stay on top of things, gain more knowledge and evolve as a deep learning person.

Here comes the list:He mentioned also that if you read:5–20 papers (in a field of choice, say speech recognition) => it may be probably enough knowledge for you to implement a speech recognition system, but maybe not enough to research or be at the cutting-edge.

50–100 papers => you probably have a very good understanding of the domain application (speech recognition).

   Don’t start reading the paper from the first to the last word.

Instead, take multiple passes through the paper, here’s how to do it:When you read a paper, try to answer the following questions:If you can answer these questions, hopefully, that will reflect that you have a good understanding of the paper.

It turns out as you read more papers, with practice you get faster.

Because, a lot of authors use common formats when writing papers.

For example, this is a common format that authors use to describe the network architecture, especially in computer vision:    It’s not unusual for people that are relatively new to machine learning to need maybe an hour to understand a relatively easy paper.

But, sometimes you may stumble across papers that takes 3 hours or even longer to really understand it.

   There are a lot of great resources online.

For example, blog posts listing the most important papers in speech recognition would be very useful if you’re new to this domain.

A lot of people try to keep up with the state-of-the-art of deep learning as it evolves rapidly.

And so, here’s where you should go:   Try to rederive it from scratch.

Although, it takes some time but it’s a very good practice.

     The most important thing to keep on learning and getting better is to learn more steadily rather than having a focus-intensive activity.

It’s better to read two papers a week for the next year than cramming everything over a short period of time.

   Whether your goal is to get a Job (big company, startup and faculty positions) or do more advanced graduate studies (maybe join a PhD program)Just focus on doing important work and consider your job as a tactic and a chance to do useful work.

  A very common pattern for successful machine learning engineers ,strong job candidates, is to develop a T-shaped knowledge base.

Meaning to have a broad understanding of many different topics in AI and very deep understanding in at least one area.

    A very efficient way to build foundational skills in these domains is through courses and reading research papers.

   You can build it by doing related projects, open-source contributions, research and internships.

   If you want to keep learning new things, here are the things that affect your success:So if you get a job offer, ask about which team you will be working with and don’t accept an offer that says “join us, and we’ll assign to a team later” because you might end up with a team that work on things that don’t interest you; which doesn’t help to evolve efficiently.

On the other hand, if you can track down a good team (even in an unknown company) and join them, you can actually learn a lot.

     I tried to summarize the key takeaways of Andrew’s advice in the following list:   I hope you find this blog fruitful and I wish you good luck with your machine learning career.

If you liked the blog, clap for it ;)And, let me know if you have any questions down in the comments.

Cheers!  Bio: Mohamed Ali Habib is a computer science graduate.

He is interested in machine learning, deep learning and data science.


Reposted with permission.

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