If you are like me, we always feel insecure about the quality of the work we do. There are brilliant people out there wh

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.

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:

This is just a quick recommendation for each domain, there are a lot of other great courses listed on my Github page. The curriculum is open-source so you can fork it, trim down the ones not relevant to you, or add more as you wish.

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.

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.

You may become a somewhat competent web developer or data scientist without knowing how the computer works, why a bunch of transistors become programmable, what it takes to make the computer understand your code, or what makes the code efficient, despite it being your workhorse and the greatest machine mankind has ever created.

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.

On top of these domains, you can pick your specialization. My interest is in natural language processing and sequence modeling, so I added a project course for it. You can come up with your own and use it as a capstone project, showcase your work to the world once you are done.

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).

On the other hand, there are cases where bottom-up is necessary. For instance, if you learned to code without learning computer science basics, you quite possibly will never encounter a problem that explicitly requires you to learn them.

Top-down learning is usually not adopted in schools. The textbooks and lectures always have an organized flow of logic. Necessary knowledge points are introduced before more advanced topics, and it truly had an effect on me — it made me feel uncomfortable dealing with a complex task in which I had gaps in knowledge. It made me naturally want to understand everything before I’d start the task. Ironically, almost all real-world problems are complex and you often don’t understand every underlying mechanism. But you don’t have to know everything to do it! The stark contrast between school and reality shows that top-down learning needs much more attention. Without it, a lot of potentially productive people don’t even start.

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.

This is harmless to an extent. Many coders who didn’t major in CS made it in the tech industry without needing to master the basics, myself included. Yet, I can tell you that it does make a difference long term. It limits the things you can do and how good you can get. So if you are like me, taking the time to learn the basics is worth it.

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).

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really close to being a replacement of C. Go pulled off the getting the simplicity and a lot of the feeling of using C. But it uses garbage collection which doesn’t make it fully C replacement. It is worth nothing that you still have more control over memory usage in Go than in Java, since you got pointers and can actually create your own secondary allocators.