A warm welcome to Daniel Seita for today’s interview. Daniel is a computer science Ph.D. student at the University of California, Berkeley. His research interests broadly lie in areas like Artificial Intelligence, Robotics, and Deep Learning. He is deeply passionate about explaining technical insights and one such favorite insight of mine from Daniel’s archive is Understanding Generative Adversarial Networks. You can check out all of his blog pieces from here. He writes on a wide range of topics and has written more than 300 such pieces.
I was approach by Paul with a cold email, and agreed to do the interview for a number of reasons:
- I am honored that my blog posts have provided him insights.
- I was impressed by the wide range of inspiring people who Paul previously interviewed.
- I wanted to indirectly provide more support to PyImageSearch because that website has been a tremendously helpful resource for my research over the last few years.
To expand on the last point, PyImageSearch is incredible, filled with tutorial
after tutorial in such plain-spoken, clear language. I typically use it as a
reference on using OpenCV to adjust or annotate images, but PyImageSearch is
also helpful for Deep Learning more broadly. For example, literally
yesterday, I was learning how to write code using TensorFlow 2.0 with the new
eager execution (I usually use PyTorch). As part of my learning process, I
read the PyImageSearch articles on
tf.keras and how to
use the new
tf.GradientTape feature. I have not had to pay anything to
read these awesome resources, though I would be willing to do so.
As I mentioned earlier, I hope you enjoy the interview. Inspired by the interview, I am working hard on blog posts here, to be released in the next few months. It’s Spring Break week now, and unlike last year when I was a teaching assistant for Berkeley’s Deep Learning class and needed to use Spring Break to catch up on research and other things, this time I’m mostly taking a breather from an intense research semester thus far.
As usual, thank you for reading, and please stay safe!