You have developed a passion and now you want to embark on a new career, but you are unsure where to start to enter the space of data science. This post will provide you with clear, practical steps to get on the road to a rewarding and stimulating career path. The...
One of the most celebrated, recent advancements in language understanding is the XLNet model from Carnegie Mellon University and Google. It takes the “best-of-both-worlds” approach by combining auto-encoding and autoregressive language modeling to achieve...
Is the grass greener on the other side? As with all things in life, there are pros and cons to moving into industry after being in academia and vice versa. I discuss the differences between the two career paths and things to consider in preparation for the transition....
Introduction Part of my blog will cover the latest and most significant developments in machine learning research. My aim in these paper readings is to extract the key points, so you can easily digest what the research is contributing. I will go into some detail into...
Data science in industry is booming, and as a result, there has been an explosion of available roles with overlapping skill sets. If you are coming across role titles like “data scientist,” “research scientist,” and “machine learning...
