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Netflix. Twitch. Linkedin. Facebook. Twitter. What do all of these big companies have in common besides their shared success? They all have an AWS backend, which allows them to scale quickly at a cheaper cost than utilizing traditional in-house servers.


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Today, there are thousands of courses out there for computer science, so it can be difficult to determine which ones are the most useful for taking and which ones will provide no value. Below are 5 platforms that I’ve found personally useful (in order of difficulty) as well as recommended courses for each of them.


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I’ll be upfront with you, getting internships is hard. Like really hard. If you don’t have the right connections or a general idea of what you want to do, you may end up trying to get one for months with no success.

Although it may be difficult, there are many things you can do to optimize your chances and produce a strong application. Below are some steps to boost your chances.

Note: Most of these steps will actually help you with other forms of employment as well, from freelance work to full-time jobs.

A Quick Background

Many of you may think that a high schooler may not be qualified to answer this, so I wanted to give a quick background about myself to show how the tips I’ve implemented below have worked out. …


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A general representation of training a Haar classifer. (Image Source)

You see facial recognition everywhere, from the security camera in the front porch of your house to your sensor on your iPhone X. But how exactly does facial recognition work to classify faces, considering the large number of features as input and the striking similarities between humans?


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A representation of a variational autoencoder. (Image Source)

Imagine this: you’ve spent forever perusing the internet for images, and you’ve finally found the perfect image to put inside of your presentation. You save the image and move it to your presentation, when you realize, the image has a watermark! You angrily start to pick up your water bottle to throw at the computer when you remember a computer program you created a while back in your AI class: the perfect way to remove watermarks, using autoencoders.

Well, that’s a bit of an understatement about what autoencoders can do, but still an important one nonetheless! Autoencoders are used in a wide variety of things from dimensionality reduction to image generation to feature extraction. Autoencoders allow you to replicate the works of Picasso, scale down terabytes of data, and denoise grainy images from security cameras. …


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A futuristic view of hyperloops with Virgin Hyperloop One. (Image Source)

Recently, I had the chance of moderating a fireside chat with Meit “Matt” Shah, who’s part of the product and technology management team at Virgin Hyperloop, one of the companies at the forefront of hyperloop technology. Throughout the talk, he shared insights into Virgin Hyperloop’s own technology and insights into the hyperloop space in general. If you’re not familiar with hyperloops, I suggest you skip down to the “Further Reading” section and check out the article there. Here are my main takeaways from the fireside chat:

Takeaway #1: Hyperloops will be invaluable in the cargo and the transportation industry.


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As a child, the thing that I always wanted to grow up to be was a doctor; I used to dress up as a doctor on Halloween and play Operation in hopes of experiencing the same thrill that doctors do when they operate. However, something that I had not fully comprehended at that time was the fact that the patients that doctors worked on were real. …


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Garry Kasparov vs. Deep Blue (Image Source)

The year was 1997. It seemed that the world chess champion, Garry Kasparov, would easily hold his own against Deep Blue, a computer algorithm that couldn’t possibly comprehend the 10¹²⁰ possibilities that a chess game could unfold in. However, rather than shorting out due to the immense number of computations it had to perform, the supercomputer Deep Blue actually ended up defeating Kasparov in the match by a score of 3.5–2.5. How could a computer possibly make these moves without trying out every single possible combination?

Intro to Reinforcement Learning

Enter reinforcement learning, a subsection of machine learning where computers learn from trial and error using past feedback to improve. Rather than using a common mapping of input and output like with supervised learning, reinforcement learning uses rewards for positive behavior and punishment for negative behavior. Computers “train” in a virtual environment where they are passed a certain state that they are in, and have to take a certain action that will maximize the reward that it will get. …


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A look into the inner workings of a quantum computer. (Image Source)

When people hear the term “quantum computing,” their minds conjure up confusing mathematical equations and complicated machinery that costs millions of dollars. Although current work into quantum computing does have those aspects, the underlying concepts of quantum computing are much simpler than this, and the understanding of the implications of this field are accessible to everyone.

Aditya Mittal

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