Writing & tutorials

Earlier tutorials and learning notes on graphs, molecular machine learning, and working with data. Publication dates reflect when these pieces were written; some libraries and external resources may have changed.

Data vs Information vs Insight

This was a session run by me as part of The Data Lab series in Multiverse. In this session, walk through some real-world scenarios where insight, data and information are used to meet business goals. At the end of the session, I give the attendees a chance to apply your learning to a group task. In this session, you’ll learn: What data, information and insights are and the difference between them. How to identify them in different business contexts

The Significance Of Knowledge Graph

I don’t know anything about conversational systems For a long time I have been wondering how a conversational agent might work, and I wasn’t been able to figure it out. The first thing I tried to do was figure out what a knowledge graph means, and I found out that the definition for it is not very clear. In particular, I could not understand the difference between knowledge base and knowledge graph. That’s why I decided to document my thoughts in this blog post, it will be a work in progress and I will keep updating them as required. ...

Graph Embeddings For Networks- Brief Introduction

The world around us is composed of systems, whether its solar system, digestive system, ecosystem or social system, they all have different entities, objects, or players that work together and share complex relationships with each other. Networks act as a general language for describing and modelling such complex systems. For instance let’s say we have 10 objects, they can be anything from people, phones or genes, we represent them as dots (or nodes). ...

Centrality in Graph Theory

One of the key points of Graph Theory is that it conveys an understanding of how things are interconnected via nodes (points where various paths meet) or edges (the paths connecting the nodes). There are a number of different types of graphs, of which the most well-known are digraphs (directed graphs, whereby A may lead to B, but the reverse may not be true), an undirected graphs (where there is no implied directionality). One of it major application is in trying to understand networks, and when graph theory is applied to complex networks the insights they reveal may seem more magic than math. For instance, Facebook (and almost all other social networks) model the users and their connections with their friends in a graph structure. And, they use this graph model to do their business by applying graph theory ideas to learn more about how to populate your timeline, what advertisements to show, etc. Google, uses a graph representation to store/retrieve the semantic relationship between different entities, which they call it, Knowledge Graph, as well as providing you with the top relevant webpages for your query, using PageRank algorithm, which is a graph theoretic algorithm. ...

Molecular Machine Learning: Introduction to building ML models to predict molecular properties.

Drug discovery is a costly and lengthy process, filled with lots of false leads and unsuccessful efforts. The pharmaceutical industry is one of the most heavily regulated industries in the world with many rules and regulations enforced by the government to protect the health and well-being of the public. The probability of a drug passing a clinical trial phase is less than 12%, most drugs failing due to safety reasons [1]. In the United States, it takes an average of 10–12 years for an experimental drug to transition from theory to practice, clinical trials account for approximately 6 years of this timeframe. The average cost to research and develop each successful drug is estimated to be $2.6 billion. This number incorporates the cost of failures — of the thousands and sometimes millions of compounds that may be screened and assessed in the early stages of R&D, only a few of which will ultimate receive approval [1]. Therefore, there is a large scope for improvement when it comes to streamlining the drug development pipeline and especially each step taken before a drug is introduced into a clinical trial. ...

How fastai makes deep learning easy: classifying cancer cells using CNN

This post is meant for people who are new to deep learning and are looking for ways to quickly build neural network models. They may not necessarily be from a math or computer science background, but have experience with python and interested in using deep learning in your own domain. Its important to know that in real life, there is no free cookies and deep learning is no exception. Yes, the basics of how it works is very simple, but to truly understand deep learning, one has to dig deeper and go beyond the basics. It may be easy to learn but its really difficult to master. The bottom line is that even though most online courses or blogposts (including this one) may give you the impression that deep learning is quiet fun and easy, it is only meant to spark interest, but one should always remember that at its core deep learning is all about one thing, maths. Nonetheless, We all need to start somewhere, so we might as well begin by making use of frameworks like fastai, to help us implement neural networks quickly, so we can visualise what’s happening and get an intuitive sense of how deep learning could help us in our own domain. ...