Yesterday, I attended the amazing “GANs for Good” panel discussion hosted by deeplearning.ai, and here are my takeaways: Generative adversarial networks (GANs) have been improved over the years and are starting to see adoption in the real world in domains such as health, art, and augmented reality. A conversation on progress and responsible use is needed. Current progress and iterations of GANs show that we have gone from generating simple low-resolution images to high-resolution realistic images. However, applications beyond simple […]

So you are interested in applied machine learning (ML) research? Oftentimes, a lot of young aspiring machine learning researchers jump straight into reading papers and either get discouraged with the amount of work published on a particular topic or get too caught up reading a lot of papers with very little progress on generating new and exciting research ideas. To avoid these situations and ensuring a healthy start on your research journey, here are some of my tips on how […]

I have always emphasized on the importance of mathematics in machine learning. Here is a compilation of resources (books, videos, and papers) to get you going. This is not an exhaustive list but I have carefully curated it based on my experience and observations. This is a repost of my Twitter thread that you can find here. I will keep updating the list here as I come across more useful resources. Mathematics for Machine Learning by Marc Peter Deisenroth, A. […]

For the last couple of months, I have been doing some research on the topic of machine learning (ML) in production. I have shared a few resources about the topic on Twitter, ranging from courses to books.  In terms of the ML in production, I have found some of the best content in books, repositories, and a few courses. Here are my recommendations for learning machine learning in production.  This is not an exhaustive list but I have carefully curated […]

Before you jump into deep learning, I would strongly advise you to do a few introductory machine learning courses to get up to speed with fundamental concepts like clustering, regression, evaluation metrics, etc.  Here is a thread including a few recent courses you can explore: This is a crosspost of a Twitter thread I published earlier this week.  Elements of AI by University of Helsinki Note: I have taken many machine learning courses online. I do some courses for fun […]

I have been studying natural language processing (NLP) since 2013, back when manual feature engineering was very popular in the world of machine learning. We have come a long way since then. I actually specialized in information retrieval and machine learning techniques for my Ph.D., particularly how they apply to social computing and computational linguistics, while at the same time developing approaches for efficient information extraction from large scale text-based data. I am fortunate to have experience with classical machine […]

Transformers have accelerated the development of new techniques and models for natural language processing (NLP) tasks. While it has mostly been used for NLP tasks, it is now seeing heavy adoption to address computer vision tasks. That makes it a very important technique to understand and be able to apply. I am aware that a lot of machine learning and NLP students and practitioners are keen on learning about transformers. Therefore, I am motivated to prepare and maintain a recipe […]

I have been doing NLP/ML research for the last 6 years. I have come across a lot of machine learning resources and papers. Today, I kept thinking about the machine learning / NLP / deep learning related blog posts (not papers) that have been transformational for me. In this blog post, I provide a short collection of a few high-impact blog posts that come to mind. This post was originally a Twitter thread. 1) The Unreasonable Effectiveness of Recurrent Neural […]