Hi, It’s Elvis here! I regularly talk, write, and tweet about data science and machine learning related topics. However, there are other interests that I would love to share through this blog including tips on writing, presenting, researching, coding, learning, etc. Specific interests range from linguistics to education to product design. I work and collaborate on a lot of different projects involving different types of technologies. I would love to share more of that in this new blog. If you […]

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 […]