#62: San Diego Technology Immersion Group Learns Python
What's it like to learn Python? Yes, some of you may have just picked up the language while others have lived and breathed it for years. Either way, you may have some hindsight bias towards the experience. What was hard? What were your expectations? What delighted you?
7 Jun 20161h 9min
#61: Free software, free people
How often do you read some news headline about free speech denied and human rights being suppressed and think that sucks but there is nothing I can do about it from my distant perspective. I guess you could vote slightly differently in the next election and maybe, just maybe, it will have a small impact in 4 years time.
30 Mai 201654min
#60: Scaling Python to 1000's of cores with Ufora
You've heard me talk previously about scaling Python and Python performance on this show. But on this episode I'm bringing you a very interesting project pushing the upper bound of Python performance for a certain class of applications.
24 Mai 20161h 7min
#59: SageMath - Open source is ready to compete in the classroom
What do you do when you are a high caliber mathematician or scientist and you want share your algorithms and code? This sounds like a job for github, but the problem is often this work is done on proprietary platforms such as Magma, Matlab, Mathematica or others.
18 Mai 201659min
#58: Create better Python programs with concurrency, libraries, and patterns
What do you focus on once you've learned the core concepts of the Python programming language and ecosystem?
10 Mai 201654min
#57: Python performance from the inside-out at Intel
When you think about the performance of your software, there is nothing more low level and fundamental than how your code executes on the CPU itself. Many of us study and try to understand how to maximize performance at this low level. But few are in a position to define what happens at this level.
5 Mai 201656min
#56: Data Science from Scratch
You likely know that Python is one of the fastest growing languages for data science. This is a discipline that combines the scientific inquiry of hypotheses and tests, the mathematical intuition of probability and statistics, the AI foundations of machine learning, a fluency in big data processing, and the Python language itself. That is a very broad set of skills we need to be good data scientists and yet each one is deep and often hard to understand.
27 Apr 201651min
#55: How our engineering environments are killing diversity (and how we can fix it)
In the software field, we pride ourselves on fairness, openness and the fact that our workplaces are largely meritocracies. And compared to other environments, I would say this is certainly true. It's one of the reasons I love being a developer. And yet, if we look at programming jobs in Silicon Valley, you'll see that over 85% of them are filled by men and less than 15% women.
21 Apr 20161h 1min
