What is a JWT and why would I want to use one?

This talk will cover common use cases. 

This talk is for those who want to pierce the veil of abstraction and learn how their Python code is actually executed on a computer. First we will start with a guided overview of the Python Run Time envioronment in the CPython interpreter. Next will be an overview of the builtin inspect package and how it allows for direct access to the python runtime in your own Python code. After which I will show how you leverage this knowledge in PDB.

A deep dive into what actually happens when you're interfacing with gpio pins at the hardware and register level in micropython

How does the elipses work? Let's find out.

Eve and Ray embarked on a two week experiment they're calling a Learning Sprint. 4 hours a day, 5 days a week over two weeks they set goals and executed on them. What did they learn? Did it work? What fun facts did they pick up along the way? They'll explain in their thrilling talk for all skill levels.

Ever been curious about the Rust programming language? This talk will describe the experience of going through the Advent of Code puzzles in Rust from the point of view of a Python user. Discover the alternatives to pip, functions and passing values, exception handling, and more.

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.

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.

Reticulate is a package for R that allows you to run Python code inside of R. Since both Python and R are very popular for common data science tasks, it makes sense that you would want to use them together. In this talk, I'll demo how to run a Python package inside of R.

Brianne Caplan is the CEO and Founder at CoderHeroes, a kid-centered “learn to code” program where kids ages 7- 18 team-up with other brave and aspiring coders to build world-changing apps. Its buy-one-give-one model means that families who pay for classes are helping to fund Code Your Dreams programs for students in underserved neighborhoods. Brianne will talk about her team's work in bringing culturally relevant coding programs to underserved youth in Chicago. She will speak about the equity challenges that exist, as well as the opportunities that exist for us all to make a difference.