Posting content is good. But posting content without seeking to get value out of it isn't good. Always do something with intention and purpose, not solely for the goal of validation. Epictetus says "Speak less, but speak more." Do the same here. You don't need to constantly post but when you do, do it well. 

This was the exact reason why I didn't do my October newsletter. It wasn't that it wasn't done well but rather it was something that I thought I wouldn't get value from. Bias towards action is something that I came across this month. At TKS, we talked about what it really means to train that activator mindset and not overthink things. What's the worst-case scenario of doing something? Well, what would be the worst-case scenario of posting the newsletter? I could gain more subscribers and have meetings with people who reached out to me, along with getting feedback to improve. Isn't that a win-win?

Bottom line is, whenever you post content, remember 2 things: are you getting value out of it? Will you get something out of what you're doing? The other thing is to remember what you're worst-case scenario of something is. If you're singing in public, your worst-case scenario is someone coming up to you and singing with you and/or complimenting you. Isn't that a win-win?

Hey there 👋 and welcome back to my monthly newsletter! Hope you enjoyed that mini-reflection at the top, it's something that's been on my mind and something that I personally thought was something worth talking about so hope you liked it! 😃. Let's talk about what I've been working on this month:

• Quantum Computing + Quantum Machine Learning
• Learning Linear Algebra, Calculus, and starting the AI "dark-side" dive
• Consulting for Instacart
• General life update

So let's get this party started 🥳🥳

Quantum Computing gives me nightmares. I wanted to first start learning about it 3 months ago and after watching the kurzgesagt video on it, I got nightmares. It was really hard for me to wrap my head around it and I wanted to avoid out of fear of me wasting time.

This month, I decided to overcome it and combine Quantum Computing with something that I'm passionate about: Artifical Intelligence. I made a video on YouTube 🎥 talking about Quantum Machine Learning. Have fun watching!

For the next 1-2 months, I've decided that I'm going to focus on the theory, math, and research papers behind Machine Learning. It's something that I've wanted to avoid at the beginning but now I think that I'm fully ready to learn everything.

I started this month by learning linear algebra. Khanacademy's 200 video playlist (seriously, I actually didn't even think that Sal Khan would make that many video's! The whole world really is grateful for him) and Grant Sanderson's (aka 3blue1brown) linear algebra playlist is what were my go-to's. Although I wasn't able to get through the 200 video's on khanacademy, these 2 resources really helped me out and I learned a lot about linear algebra from computing things like reduced row echelon form to eigenvectors!

Part 2 of my month was deepdiving into research papers. I wanted to focus on reading research papers and understand them. I went through Lex Fridman's research papers (really, really admirable! Check out the one on Arguing Machines, it's really dope 😎) and they've really sparked my interest in semi-autonomous and autonomous driving. But, like with any other research paper, I couldn't overcome a math obstacle.

I decided to spend some time to go back to the math and figure it out. That's when I decided that there was a lot of theory that I was missing. I knew the logic behind algorithms, not the math. If I wanted to really understand ML, I need to understand the math behind these algorithms.

That's exactly where I stumbled upon Andriy Burkov and his 100-page Machine Learning book upon Liam Hinzman's recommendation (thx a bunch Liam!💖) and it's an absolute goldmine 🥇. Although there are some parts of math I can't understand, I was able to figure it out. Andriy writes the book in such a way that a kid like me who just finished grade 9 math can understand it. His explanation of derivatives and gradients is pure gold. He's the real definition of a Machine Learning expert: someone who can explain something in a digestible manner to even people like me.

I'm currently going through the book right now (not done yet! making sure that I understand it properly) and am planning to finish it later next month.

Ok one more paragraph 😅. I've also started focusing on reaching out to people who are ML engineers. Reaching out is something that I'm scared of. Thx Harrison Nolan for pushing me to do this! I've really met some really cool people so make sure to read 'till the end to find out more about them!

This month, I had the opportunity to consult with Instacart on some of the biggest problems they're facing right now. My team was focused on finding a way to improve customer satisfaction. We focused on improving shopper satisfaction and giving them an incentive to perform better to help therefore increase customer satisfaction.

Overall, this was an extremely great learning experience. I learned a lot more about developing recommendation decks, how to break down problems using techniques such as root cause analysis and MECE along with understanding how we can derive a legitimate solution and playing around with the math behind our solution.

Shoutout to my team: Mikael Haji, Elizabeth Trykin, and Francesca Bizzarri. It was great working with you all 💖!

Last month (well, September), I talked about getting another super super legit project done. Well, I did! My team (back in October) participated in an AI hackathon. We were to come up with a problem and find a solution to it using AI. In our case, we decided to tackle the problem of distracted driving. The model that I was able to make had accuracies greater than 99% 🤯 and I actually was able to run it in my car!

I also talked about joining the 5am club! Well, I was actually able to join it! I have been waking up at 5am on most days (we don't talk about the other ones...😅) and I've really felt a lot more productive. One thing I'd say is also to make sure that you sleep early. I usually sleep between 9:30 and 10:30 to ensure that I have enough sleep for me to be able to function the next day.

The other (and final!) goal was to cold email AI experts and I did! I sent out around 15 emails and was extremely grateful to have the 3 that I had this month! I had my calls with Rikard Saqe, Sayantan Chattopadhyay, and Brendan McGivern. Shoutout to these 3 for the awesome calls and advice that they gave!

I've also been working out and listening to podcasts a lot more. Lex Fridman's podcasts really turn me up and are something that I'd 110% recommend for anyone really into AI to listen to. Some really interesting thoughts on the future of humanity and intelligence as a whole (he's even got Elon Musk on the podcast, twice!). 

I also updated my personal website and added a new blog section to it. It'll have a bunch of personal thoughts and anything that I find interesting to talk about so feel free to take a look!

Well, thanks for reading this update! Hope that you were able to get something out of it! See you in December (otherwise known as the end of 2020!!!!!)