A breakdown of the inner workings of GNNs…

TLDR; Here, I cover the basic intuitions and mechanisms of Graph Neural Networks. Using colourful diagrams, I try to condense the essential steps needed to learn over structured graph data.

Nickelback approves of Graph Deep Learning 😎

Graph Deep Learning (GDL) is an up-and-coming area of study. It’s super useful when learning over and analysing graph data. Here, I’ll cover the basics of a simple Graph Neural Network (GNN) and the intuition behind its inner workings. Don’t worry, there are tons of colourful diagrams for you to visualise what’s happening!

A graph is a data structure comprising of nodes (vertices) and edges connected together to represent information…


Getting users to fall in love with your open-source project

In this mini-article, I’ll be covering the basics of writing a decent README for all the open-source beginners out there. The process is fairly simple and requires effort on your part.

There are several components to a README that are necessary for ensuring your project has a positive outreach. I’ll be covering each component by likening it to the README I made for gpt2-client, a project I made that has over 13K downloads in less than 2 months.

READMEs are powerful. Very powerful. Knowing how to craft one might just make your project much more likable.

A minimal cover picture…


I trained an Neural Machine Translation(NMT) model on a TPU and now feel like a Wizard…

You have a plain old TensorFlow model that’s too computationally expensive to train on your standard-issue work laptop. I get it. I’ve been there too, and if I’m being honest, seeing my laptop crash twice in a row after trying to train a model on it is painful to watch.

In this article, I’ll be breaking down the steps on how to train any model on a TPU in the cloud using Google Colab. After this, you’ll never want to touch your clunky CPU ever again, believe me.

TL;DR: This article shows you how easy it is to train any…


An intuitive guide filled with code and colorful diagrams…

For a crash course on Quantum Computing, do check out the previous article.

TLDR: Here, I talk about Qiskit, an open-source Python module for building quantum circuits and simulating operations on Qubits. We’ll look through sample code and then move on to an in-depth explanation of what the code does with very intuitive, colorful diagrams!

In my previous article, I mentioned that models can be built to simulate processes of interest in the universe. …


Almost everything you need to know about Quantum Computing explained using very intuitive drawings…

Have a quantum cat

Quantum Computing is a relatively new, up-and-coming field that smashes the concepts of Quantum Physics, or the study or very small things, and Computer Science.

In this article, I’ll introduce you to the concepts that drive this young, growing field with intuitive analogies and as always, colorful sketches and drawings.

Note: I won’t be covering the math and vector notations in this article for simplicity purposes. If interested in reading such an article, drop a comment below or feel free to contact me via Twitter!

Classical CS involves the flow and manipulation of bits (binary digits) — the basic units…


A general guide on what makes the Markov Decision Process tick

tldr; This is part of a series of reference articles teaching the concepts that drive Reinforcement Learning. We’ll be starting off simple, incrementally moving on to more complex concepts further down the road.

A Markov Chain is a stochastic (always changing) model that is used to predict/estimate/guess the outcome of an event given only the previous state and its action. The state refers to the current ‘slice’ of the environment. It’s the particular condition that an agent is contained in at a specific time step.

For example, in Sonic the Hedgehog, a state refers to the current pixel intensity values…


From idea to execution, I tried to set a path for myself, met interesting folks, and learned lessons along the way…

Note: This isn’t the usual technical review or write-up about a side-project. I’m trying something different here. Please do give it a read.

TL;DR: We won the prize money for the ‘Most Innovative Startup’ idea. This is an article where I reflect on the past two years and how it all contributed towards the victory. I talk about the lessons I learned over this period of time and how you can apply it to your situation.

Something to entice you: the team comprising of Sarvasv (left), Akshith (right), and myself (center) with the Director of NTUitive (far left) and Guest of Honour, Ex-Parliament member Inderjit Singh (far right).

Growing up, I always dreamed of making it big (who hasn’t). I was fortunate to grow up in a time when technology was rapidly…


On the Origin of Genetic Algorithms

Charles Darwin, 19th century evolution theorist and author of the book, ‘On the Origin of Species’

Original article by Rishabh Anand

In the mid 19th century, Charles Darwin postulated the theory of evolution and how it played a key role in enabling organisms to adapt to their environments through natural selection – a process where the fittest in a given population survive and live long enough to pass on their traits and characteristics to future generations to ensure their survival.

Presently, Machine Learning (ML) has kicked off a new era of smarter machines capable of making better decisions compared to their rule-based counterparts from the late 90’s and early 2000’s.

Harnessing the sheer amount of computational…


Detailed review of GANs and how to waste your time with them…

I’ve recently been reading up on Generative Adversarial Networks (GANs) and I knew that I had to come up with an interesting way to use it. It was then that I noticed all the buzz about the Quick, Draw! dataset published by the Google Brain team for open source tinkering and fooling around. I know, I’m slow…

I converted the cats.npy file into training and testing sets. Here are a few instances from the training set. This is how people around the globe perceive cat faces to look like. It’s interesting to see various representations of cat faces from different perspectives!

GAN stands for Generative Adversarial Network. It is a model that is essentially a cop and robber zero-sum game where the robber tries to create fake bank notes in an effort to fully replicate the real ones, while the cop discriminates between the…


Running ML models on the browser became so much more easier!

We have seen the awesome capacity of TensorFlow and how it makes the process of programming neural network architectures so much more simpler. An increasing number of startups are using TensorFlow in their open-source projects.

On Friday, 30 March 2018, the TensorFlow team announced the arrival of the much-awaited web version of the famous ML framework, TensorFlow.js.

Now, developers can build lightweight models and directly run them on the browser without any hassle. …

Rishabh Anand

ML Research Student @ NUS • NLP Ninja • Technical Writer • Open-source Jedi • https://rish-16.github.io

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