The Feynman Technique is one method to make us remember what we read by using elaboration and association concepts. It’s

Nobel Prize-winning physicist Richard Feynman (1918–1988) was an expert for remembering what he learned. Bill Gates was so inspired by his pedagogy that he named Feynman, “the greatest teacher I never had.”

In my case, Docker was very important in deployment. It helps me deploy multiple modules in containers. Apart from that, Tensorflow Serving is also very useful to easily make the trained model can serve prediction requests.

Another important skill as a machine learning engineer is that you need to be able to build end-to-end systems. More importantly if you are working on multiple models at the same time, it will be much easier when using the pipeline. Loading datasets, preprocessing and feature extraction, training and evaluating models, and making predictions can be simplified using just a single command.

It was Schopenhauer who already stated in the 1850s, “When we read, another person thinks for us: we merely repeat his mental process.” So to learn, we need to think by ourselves.

To protect ourselves from overstimulation, our brains filter and forget most of what we consume. If we remembered everything we absorb, we wouldn’t be able to operate in our world.

Feynman mastered this process like no other. The people of his time knew him for being able to explain the most complex processes in the simplest language. They nicknamed Feynman “The Great Explainer.”

But most people act like their brains would keep everything. They focus on reading a specific number of books a year. By focusing on quantity, instead of learning, they forget anything they read. Ultimately, for them, reading is mere entertainment.

Write not only for yourself, but also for others. Creating documentation can be helpful for other engineers if they want to keep experimenting or just use your program. It would be nice if the documentation is also easy to read and understand by people outside our expertise.

Don’t expect that you will get satisfying results right away in just one try. In addition, doing repetitive things for a long time can indeed be stressful. Therefore it is very important to automate your machine learning workflows to shorten your time and reduce stress.

Teaching is the most effective way to embed information in your mind. Plus, it’s an easy way to check whether you’ve remembered what you read. Because before you teach, you have to take several steps: filter relevant information, organize this information, and articulate them using your own vocabulary.

I was one of those people who said “It worked on my machine!” when there was an error deploying our system to the production. And again, I hated myself for that. For this reason, it is very important to know some useful frameworks and tools for deployment. After doing some exploration, you can compare them and choose which one is most suitable based on your needs.

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xpert Laura Vanderkam writes in her Forge column today, “The upside of acknowledging that you aren’t going to do something is that then you can accommodate this truth. … Throw yourself into your current responsibilities and the things you really are motivated to work on rather than burning brainpower worrying about ones you don’t want.”

You can spot these people easily. For example, they say they’ve read a book, but lack the words to explain their takeaways. Likely, they haven’t learned a thing from reading it.

Good documentation usually contains information such as configuration of hyperparameters, epochs, methods, data, and scores. In addition, providing automated scripts to run programs is helpful for other engineers without having to read the entire code first.

If you’re after a way to supercharge your learning and become smarter, The Feynman Technique might just be the best way to learn absolutely anything. You can think of it as an algorithm for guaranteed learning.