Topics

• ChiPy Mentorship Returns
  By: Ben Xia-Reinert
  Experience Level: Novice
  Length: 15 Minutes
  Description:

  The new ChiPy Mentorship site is going live on July 4th. While the structured, 13-week ChiPy Mentorship program is not returning, this app will enable members of the Chicago Python community who wish to be mentees and mentors to find and connect with each other. In this talk, we will explain how the app was made, what we are looking to accomplish, and how you can be a part of it.

• Ten Ways to Fizz Buzz
  By: Joel Grus
  Experience Level: Novice
  Length: 15 Minutes
  Description:

  Fizz Buzz is the following (simple) problem:
  
  Print the numbers from 1 to 100, except that if the number is divisible by 3, instead print "fizz"; if the number is divisible by 5, instead print "buzz"; and if the number is divisible by 15, instead print "fizzbuzz".
  
  My association with this problem began in 2016, when I wrote a blog post called Fizz Buzz in Tensorflow, the (possibly fictional) story of one such insulted programmer who decided to show up his interviewer by approaching Fizz Buzz as a deep learning problem. This post went modestly viral, and ever since then I have been seen as a thought leader in the Fizz Buzz space.
  
  Accordingly, over the years I have come up with and/or collected various other stupid and/or clever ways of solving Fizz Buzz. I have not blogged about them, as I am not the sort of person who beats a joke to death, but occasionally I will tweet about them, and recently in response someone suggested that I write a book on "100 Ways of Writing Fizz Buzz in Python."
  
  Now, I could probably come up with 100 ways of solving Fizz Buzz, but most of them would not be very interesting. Luckily for you, I was able to come up with 10 that are interesting in various ways, which I will barrel through in 15 minutes or less.

• Introduction to AutoML
  By: Paco Nathan
  Experience Level: Novice
  Length: 60 Minutes
  Description:

  AutoML is a very active area of AI research in academia as well as R&D work in industry. The public cloud vendors each promote some form of AutoML service. Tech unicorns have been developing AutoML services for their data platforms. Many different open source projects are available, which provide interesting new approaches.

  But what does AutoML mean? Ostensibly automated machine learning will help put ML capabilities into the hands of non-experts, help improve the efficiency of ML workflows, and accelerate AI research overall. While in the long-term AutoML services promise to automate the end-to-end process of applying ML in real-world business use cases, what are the capabilities and limitations in the near-term?

  This talk surveys the landscape and history for projects and research efforts related to AutoML, looking beyond just hyperparameter optimization and considering the impact on end-to-end workflows and data science practices. We'll show sample code using different open source projects and provide pointers to online resources to learn more.