Why Deep Learning is everywhere [Math Mondays]

And why Neural Networks get so much attention

Oct 25, 2022

To learn more about the newsletter, check our detailed About Page + FAQs

To help me understand you better, please fill out this anonymous, 2-min survey. If you liked this post, make sure you hit the heart icon in this email.

Recommend this publication to Substack over here

Take the next step by subscribing here

Deep Learning is everywhere.

But not many people can actually explain what it is. How is Deep Learning different from Machine Learning? Or why Neural Networks became the poster child for AI even though we had so many kinds of architectures. What is the mathematical reason that AI researchers scale up Neural Networks but not Logistic Regression?

Today, we will be covering that. Even if you’re not into Machine Learning, this will help you understand the background behind one of the most important fields in our society.

Important Details

What is Machine Learning- To those of you that absolutely know nothing about this field, here is an ELI5 explanation- In Machine Learning, we feed a lot of data to the model and have it try to spot patterns. The feeding of the data is called ‘training’. In a more mathematical sense, during our model training our model tries to find a function to match the data given to it.
What is Deep Learning- Deep Learning is a subset of Machine Learning. In it, we use Neural Networks (which are basically weighted graphs) with lots of layers. However, there is a catch. Simple neural networks (even very big ones) still produce linear functions. This is true for most Machine Learning methods. However, turns out that introducing non-linearity is a game changer.
Wait….What is Linearity? How can a graph be Linear?- Here, I’m using the mathematical definition of a linear function. In simple words, a function is linear if you it meets two conditions-

Linearity is important enough as a concept that I will do a dedicated Monday post on it. It’s a concept you will see show up a lot, in all avenues of statistics and engineering.

So what does this have to do with Deep Networks- Turns out that we can approximate any continuous function by taking a combination of non-linear functions. I’m highlighting continuous b/c many people misunderstand this theorem to assume that you can approximate any function. This faulty assumption has wasted a lot of time, energy, and VC money. Very big deep networks work because they are able to try a lot of non-linear functions and all kinds of combinations of them.
To any of you that are interested in the Mathematical proof, read this.
Why everyone is using Deep Learning- The reason we see them everywhere is that humans are attracted to shiny new objects. And we’re lazy. If I’m given a data-analysis problem, I can use Deep Learning to get some kind of a result, so people often resort to them without thinking too much. And since it’s the trendy thing these days, no questions about whether Deep Learning makes sense. The whole ethos of my Machine Learning content is to highlight that you can do lots of things to improve your data analysis that don’t involve more training data, bigger Deep networks, and more training.

This should be a reasonably good overview of why Deep Learning seems to have taken over much of AI these days. If you’re a decision maker then knowing this is crucial to understanding this new field. And if you’re a developer (especially if you’re into Data Analysis), then understanding the universal approximation is crucial to understanding why you see Neural Networks everywhere.

I created Technology Made Simple using new techniques discovered through tutoring multiple people into top tech firms. The newsletter is designed to help you succeed, saving you from hours wasted on the Leetcode grind. I have a 100% satisfaction policy, so you can try it out at no risk to you. You can read the FAQs and find out more here. Use the button below to get 20% off for upto a whole year.

Get 20% off for 1 year

Before proceeding, if you have enjoyed this post so far, please make sure you like it (the little heart button in the email/post). I also have a special request for you.

Special Request

This newsletter has received a lot of love. If you haven’t already, I would really appreciate it if you could take 5 seconds to let Substack know that they should feature this publication on their pages. This will allow more people to see the newsletter.

There is a simple form in Substack that you can fill up for it. Here it is. Thank you.

https://docs.google.com/forms/d/e/1FAIpQLScs-yyToUvWUXIUuIfxz17dmZfzpNp5g7Gw7JUgzbFEhSxsvw/viewform

To get your Substack URL, follow the following steps-

Open - https://substack.com/
If you haven’t already, log in with your email.
In the top right corner, you will see your icon. Click on it. You will see the drop-down. Click on your name/profile. That will show you the link.

Click on the circled place to get your URL

You will be redirected to your URL. Please put that in to the survey. Appreciate your help.

In the comments below, share what topic you want to focus on. I’d be interested in learning and will cover them. To learn more about the newsletter, check our detailed About Page + FAQs

If you liked this post, make sure you fill out this survey. It’s anonymous and will take 2 minutes of your time. It will help me understand you better, allowing for better content.

https://forms.gle/XfTXSjnC8W2wR9qT9

I see you living the dream.

Go kill all and Stay Woke,

Devansh <3

To make sure you get the most out of Math Mondays, make sure you’re checking in the rest of the days as well. Leverage all the techniques I have discovered through my successful tutoring to easily succeed in your interviews and save your time and energy by joining the premium subscribers down below. Get a discount (for a whole year) using the button below

Get 20% off for 1 year