How to learn faster

And remember better

How to learn faster
Photo by Chris Benson on Unsplash

We consume information from various sources these days - articles, YouTube videos, newspapers, and books. But how much of this information actually stays in our mind?

I was reading a book on AI frameworks in the beginning of the year. Then life got in the way. I got a job, and learning material that wasn't relevant to my job took the backseat for a while.

A few days back, I decided to go back to the book and continue where I left off. To my complete bewilderment, I was unable to grasp many topics in the chapter I continued from. I had forgotten a lot of the concepts I previously learnt.

"Well, that's okay," I thought to myself. "Let me refresh my memory on these topics by reading the previous chapter."

I went back to the previous chapter, only to find that it was difficult to keep up with the material there too, since I'd forgotten a lot of the terms introduced in the earlier chapters.

At the end, I had to start over and skim through the material from the beginning of the book to refresh my memory. Of course, it didn't take that long to learn as compared to the first time I studied, but it was pretty time consuming. It was also quite difficult to gain motivation to start over from the first chapter when I was almost done with half the book.

If you're trying to learn a subject on your own, you've probably been in a situation like this too. This can be incredibly frustrating, because it slows down progress when we don't retain things we learnt.

Why does this happen?

Of course, time lapse and lack of repetition are two of the main factors that cause you to forget topics you learnt.

However, you can improve your ability to retain concepts by practicing a technique called deep learning.

According to Nishant Kasibhatla, a Guiness Record Holder and Grand Master of Memory, there are two types of learning - deep and shallow.

You might have heard of the world deep and shallow learning being used in the context of AI.

These words, however, can also be used to describe the way humans process information.

Shallow learning is what most of us do. It comprises of learning with more input, and less output. When you read a book or watch a documentary, you are practicing shallow learning.

You are consuming content without any kind of outlet. At the end, you would have forgotten most of what you've learnt, and only around 20% remains in your mind.

Deep learning is a technique that involves some kind of output. Reiterating concepts learnt is necessary to commit them to memory.

When I was in high school, I was studying a syllabus called CBSE. This is an Indian syllabus, and the board exams are one of the toughest in the world.

I would stay up all night studying, then spent a day or two helping my friends learn. I would teach them topics they didn't understand and break concepts down in a simpler way. This not only benefitted them, it helped me a lot too.

Repeating the topics I learnt helped me commit them to memory, and I was a lot more confident about my answers during the exams.

Explaining these concepts to another person meant I had to answer their questions, do my own research, and fill the gaps in my knowledge. This, as a result, strengthened my understanding of these topics.

You might have heard of studies indicating that we retain only around 10% of what we read but 90% of what we teach others. While there is a lot of debate around this statistic and whether or not its true, I can personally vouch for the fact that teaching doubles my understanding of a topic and helps me retain it in memory.

If you're trying to consume information, simply reading it isn't sufficient. To commit these concepts to memory and gain long-term value from it, there needs to be three things - repetition, output, and consistency.

I've looked back at old code done about a year ago and completely forgotten what I did there. It didn't even look like my code, and when I tried making minor changes, I kept running into errors.

Consistency is important. I'd completely forgotten the function of every library I used in the code because I never used them again once I moved on from the project. The lack of repetition and consistency led me to forget, and I had to learn all over again.

Output is also important. I forgot almost everything I learnt from the AI textbook because there was no output. I kept reading and spent a lot of time processing information, I never had an outlet for it. I never actually applied the concepts learnt or tried to break it down by explaining it, I simply consumed.

And while that is the way most of us learn, it certainly isn't the best.

Nowadays, I create tutorials and projects around the concepts I learn. When I explain these topics and break them down for other people, I'm actually breaking them down for myself.

When I want to recreate some old code I used to build a dashboard in Python, I can easily go back to one of my tutorials to better understand what I was trying to do. This saves time and effort, and I will be able to commit to memory for a longer period of time.