This is harmless to an extent. Many coders who didn’t major in CS made it in the tech industry without needing to master

Don’t do this as a university degree. You don’t need to finish one course after another religiously in order to graduate. Think about it, what are the things you can still remember from your school days? It could just be a handful of mental models and a personal way of learning new things. The reality is, you won’t remember much from this curriculum either. So do this instead:

When you are starting out, set some humble goals to feel good about your progress. If you get discouraged too often, you won’t be able to make it through this long journey. You have to enjoy it to go on.

Algorithm is another form of math. When you learn the likes of depth-first search or breadth-first search, you can generalize and solve problems that are difficult to solve otherwise.

Another example that favors bottom-up learning is math — the core of machine learning. Math is the subject where you work with a set of axioms, operations, and theorems as building blocks to solve new problems you’ve never seen before. The magic about math is that you can pretty much solve any problem once you master the logical building blocks, it’s just a matter of how to use the building blocks to get there (this statement has a caveat, Google “computability theory” if you are a nerd).

For example, there’s no need to feel insecure if you are not familiar with Spark when you don’t use it in your job. Instead, learn the basics of distributed computing. It will supercharge your future learning of any big data framework.

A week or so ago, I had a chance to catch up with my old roommate. Christophe no longer takes his daily bite from the Big Apple. Instead, he’s now living in Boston as a “Harvard Man.”

Learning a new thing is an investment. It is a much better investment to first learn the fundamentals well because they stay the same and won’t be outdated for a very long time.

When dealing with a complex system, it is not always possible to decompose it into clear building blocks that follow a strict line of logic. In this case, top-down is better. You try what works and what doesn’t in order to get a high-level feel of how to do it correctly. Examples include learning to play tennis, to swim, to recognize an object in an image (computer vision), or to build a new product (entrepreneurship).

Hopefully, this has been helpful to some of you who are starting out in your ML career. In the next few months, I will go back to posting technical posts to share the mental models of the topics I learned. So stay tuned!

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Allocator, n: u32) !Array(u32) { var x = n; var digits = Array(u32).init(alloc); errdefer digits.deinit(); while (x >= 10) { try digits.append(x % 10); x = x / 10; } try digits.append(x); return digits; }

If you are like me, we always feel insecure about the quality of the work we do. There are brilliant people out there who are more experienced and knowledgeable, what if my work is not good enough and people will laugh at me?

When you are used to curating good resources, you may get distracted every now and then just to find new things to collect. In the field of machine learning, there are countless new papers, new tutorials, new tools coming out every day. If you constantly chase them, you will feel insecure forever.

Another reason that showcasing your work is crucial is that the current ML hiring process is broken. Many companies can’t find qualified candidates, and candidates can’t stand out just with their resume. With a portfolio, you not only show that you are competent in what you do, you also show your passion and dedication in this field. All significant events in life are unpredictable. It may surprise you with exceptional opportunities someday!

The truth is, people don’t care. We shouldn’t take ourselves too seriously either. Everybody is a student. If someone doesn’t think they are a student, they are not growing.

Hopefully, this has been helpful to some of you who are starting out in your ML career. In the next few months, I will go back to posting technical posts to share the mental models of the topics I learned. So stay tuned!