This week we welcome Yuxi (Hayden) Liu as our PyDev of the Week! Hayden is the author of Python Machine Learning By Example and other books. You can connect with Hayden on LinkedIn.
Now let’s spend some time getting to know Hayden better!
Can you tell us a little about yourself (hobbies, education, etc):
I am currently a Software Engineer, Machine Learning at Google. Previously I worked as a machine learning scientist in a variety of data-driven domains and applied my ML expertise in computational advertising, marketing and cybersecurity. I am an author of a series of machine learning books and an education enthusiast. My first book, Python Machine Learning by Example, ranked the #1 bestseller in Amazon in 2017 and 2018, and was translated into many different languages. And I earned my degree from the University of Toronto.
I enjoy hiking a lot, so I am grateful SF Bay Area has many hiking trails complemented nicely by consistently sunny weather.
Why did you start using Python?
In 2008 when I did my undergrad.
What other programming languages do you know and which is your favorite?
C++, Java, Go, etc. Of course Python has always been my favorite.
What projects are you working on now?
I am now developing and improving the machine learning models and systems for ads optimization on the largest search engine in the world.
Which Python libraries are your favorite (core or 3rd party)?
So many, Numpy, Scipy, Pandas, TensorFlow, PyTorch, matplotlib, gensim, scikit-learn, XGBoost
How did your book, Python Machine Learning by Example, come about?
As I look in retrospect to my years of research and industrial experience, I always find learning by example is the quickest way to pick up new techniques. I am sure most readers resonate with me. For example, when you learn how to code in Python, you won’t start with a 800-page Python syntax book. Instead, you would start with printing out “Hello World.”. If you want to learn about reinforcement learning, you will first look at how it is used in real examples rather than diving into its mathematical underpinnings. Learning ML is of no difference. In my opinion, learning by example is the most efficient and intriguing approach. My goal was to write an example-heavy book that can become the most comprehensive gateway into the world of practical machine learning.
Is there anything else you’d like to say?
If you love to chat about ML, data and Python, feel free to connect with me at LinkedIn. Here is my Amazon Author page. If you have any topics you want me to write about, feel free to let me know as well.
Thanks for doing the interview, Hayden!
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