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Building Your Foundation: Why Reading Papers is Essential for Machine Learning Engineers

6 min read

Background

Based on ML and it can also apply to other fields. I realize that most of the ideas comes from reading scientific papers and if you are starting your journey in machine learning you need to build a habit of reading papers. Yes there are other sources to get ideas and understand a topic but not all topics will be covered by all sources. The main source of new techiniques are in papers. Why am I ranting out this way because I didnt want to reading them but I reallized that I NEED to read them if I want to be a good engineer.

# Machine Learning Engineer
# 1. Download a paper
# 2. Implement the paper
# 3. Keep doing this until you have a skill 

# ----------Gergo Hortz------------------

What Insipired Me to Read Papers

How to read a paper

  • Start with the Abstract
    Read the abstract, look at the main ideas of the paper and the main results. Does the paper interest you?, Is it something you want to know more about? If you are interested, you should read the paper.

  • Reading Flow
    Read the paper from start to end obviously you can pick your own pace and how you want. But I would recommend undersand the term that are used. Stop and search the meaning of the terms. This is the point where you start mastering the way to read a paper.

  • Undersand and start implementation.
    If you are not familiar with the math, you should stop and search the meaning of the terms. You will have a back and forth reading and implementing. But that is the point of I strongly recommend to try visualizing the ideas presented.

See its not hard after all. I promise you, you will get better at it when you are in your 3rd or nth paper.

Papers to start with

If you are new to reading papers but you have some basic knowledge of machine learning, you can start with the following papers:

This will cover Vision, Language, and Robotics. The good thing is papers have alot of citations so you can find more papers (ideas) where all of them are related to the same topic.

Responsibilties

Now lets talk about the responsibilty of the titles. Software Engineer, Data Scientist, Machine Learning Engineer, etc. What I usually tell my self is the longer the title the greate the responsibilty. It may apply to some titles or not. As you dive deeper into papers, you'll experience what psychologists call the Dunning-Kruger effect. Initially, you might feel like you understand everything. Then, as you encounter more complex papers, you'll realize how much you don't know. This is normal and healthy.

"Dunnning-Kruger Effect"

End of the day

Reading papers isn't just about staying current—it's about developing the deep understanding that separates good engineers from great ones. Every breakthrough in machine learning started as someone's research paper. By building a habit of reading and implementing papers, you're connecting directly to the source of innovation in our field.
Everybody journey is different but i think what I have experienced is that the more you read the more you will realize that you know close to nothing unitl you master it. I hope this post will inspire you to start and explore your interests. The only question is: when will you start? Happy Learning!.

More Info

How to Read Math in Deep Learning Paper?
How to Read Deep Learning Paper as a Software Engineer