One of the biggest misconceptions regarding education is that its main purpose is to give knowledge you can immediately use.
It is not.
The best thing education can give you is the mental agility to obtain knowledge at the speed of light.
Let's unpack this idea a bit!
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Consider a course where you build a custom neural network framework with NumPy.
This is hardly usable in practice: working with a custom library is insane.
However, if you know how they are built, you only need to learn the interface to master an actual framework!
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By understanding how the framework is built and how the underlying algorithms work, you'll be able to do much more: experiment with custom optimizers, implement your own layers, etc.
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Immediately usable knowledge gets obsolete fast.
Suppose that instead of understanding fundamentals, you only know how to use TensorFlow, but not what is inside.
A new framework comes, and you'll have to start (almost) from the beginning.
You'll always play catch-up.
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This is true for other fields like web development.
You can learn how to use the fanciest new frameworks, but when the next one comes ten minutes later, you'll be in a hard place without solid fundamentals like HTML and JavaScript.
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That's not to say that practical knowledge is inferior to fundamental knowledge.
Both are equally essential!
In general, there are two learning paths towards mastering a subject: top-down and bottom-up.
University is bottom-up, learning on the job is top-down.
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No matter which one you choose, at the end of each learning path, you'll reach mastery. Don't dismiss any option.
Both have their pros and cons, and if you are at the beginning, make sure to make a conscious decision.
(Instead of following trends and influencers.)
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Machine learning has enabled scientific breakthroughs in several fields.
Biotechnology is one of the most fascinating, as researchers could perform mindblowing tasks with the new tools.
Here are my favorite problems that machine learning helps to solve!
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These are the topics we are going to talk about:
1. Predicting protein structure from amino acid sequences. 2. Accelerating high-throughput screening for drug discovery. 3. Mapping out the human cell atlas. 4. Precision medicine.
Let's dive in!
1. Predicting protein structure from amino acid sequences.
Proteins are the workhorses of biology. In our body, myriads of processes are controlled by proteins. They enable life. Yet compared to their importance, we know so little about them!
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As you probably know, I love explaining complex machine learning concepts simply. I have collected some of my past threads for you to make sure you don't miss out on them.