The podcast about Python and the people who make it great
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Making A Difference Through Software With Eric Schles
Software development is a skill that can create value and reduce drudgery in a wide variety of contexts. Sometimes the causes that are most in need of software expertise are also the least able to pay for it. By volunteering our time and abilities to causes that we believe in, we can help make a tangible difference in the world. In this episode Eric Schles describes his experiences working on social justice initiatives and the types of work that proved to be the most helpful to the groups that he was working with.
Asking Questions From Data Using Active Learning with Tivadar Danka
One of the challenges of machine learning is obtaining large enough volumes of well labelled data. An approach to mitigate the effort required for labelling data sets is active learning, in which outliers are identified and labelled by domain experts. In this episode Tivadar Danka describes how he built modAL to bring active learning to bioinformatics. He is using it for doing human in the loop training of models to detect cell phenotypes with massive unlabelled datasets. He explains how the library works, how he designed it to be modular for a broad set of use cases, and how you can use it for training models of your own.
Great Expectations For Your Data Pipelines with Abe Gong and James Campbell
Testing is a critical activity in all software projects, but one that is often neglected in data pipelines. The complexities introduced by the inherent statefulness of the problem domain and the interdependencies between systems contribute to make pipeline testing difficult to manage. To make this endeavor more manageable Abe Gong and James Campbell have created Great Expectations. In this episode they discuss how you can use the project to create tests in the exploratory phase of building a pipeline and leverage those to monitor your systems in production. They also discussed how Great Expectations works, the difficulties associated with pipeline testing and managing associated technical debt, and their future plans for the project.
Exploring Color Theory In Python With Thomas Mansencal
We take it for granted every day, but creating and displaying vivid colors in our digital media is a complicated and often difficult process. There are different ways to represent color, the ways in which they are displayed can cause them to look different, and translating between systems can cause losses of information. To simplify the process of working with color information in code Thomas Mansencal wrote the Colour project. In this episode we discuss his motiviation for creating and sharing his library, how it works to translate and manage color representations, and how it can be used in your projects.
Destroy All Software With Gary Bernhardt
Many developers enter the market from backgrounds that don't involve a computer science degree, which can lead to blind spots of how to approach certain types of problems. Gary Bernhardt produces screen casts and articles that aim to teach these principles with code to make them approachable and easy to understand. In this episode Gary discusses his views on the state of software education, both in academia and bootcamps, the theoretical concepts that he finds most useful in his work, and some thoughts on how to build better software.
Scaling Deep Learning Using Polyaxon with Mourad Mourafiq
With libraries such as Tensorflow, PyTorch, scikit-learn, and MXNet being released it is easier than ever to start a deep learning project. Unfortunately, it is still difficult to manage scaling and reproduction of training for these projects. Mourad Mourafiq built Polyaxon on top of Kubernetes to address this shortcoming. In this episode he shares his reasons for starting the project, how it works, and how you can start using it today.
Electricity Map: Real Time Visibility of Power Generation with Olivier Corradi
One of the biggest issues facing us is the availability of sustainable energy sources. As individuals and energy consumers it is often difficult to understand how we can make informed choices about energy use to reduce our impact on the environment. Electricity Map is a project that provides up to date and historical information about the balance of how the energy we are using is being produced. In this episode Olivier Corradi discusses his motivation for creating Electricity Map, how it is built, and his goals for the project and his other work at Tomorrow Co.
Building And Growing Nylas with Christine Spang
Email is one of the oldest methods of communication that is still in use on the internet today. Despite many attempts at building a replacement and predictions of its demise we are sending more email now than ever. Recognizing that the venerable inbox is still an important repository of information, Christine Spang co-founded Nylas to integrate your mail with the rest of your tools, rather than just replacing it. In this episode Christine discusses how Nylas is built, how it is being used, and how she has helped to grow a successful business with a strong focus on diversity and inclusion.
Synthetic Data Generation Using Mimesis with Nikita Sobolev
Most applications require data to operate on in order to function, but sometimes that data is hard to come by, so why not just make it up? Mimesis is a library for randomly generating data of different types, such as names, addresses, and credit card numbers, so that you can use it for testing, anonymizing real data, or for placeholders. This week Nikita Sobolev discusses how the project got started, the challenges that it has posed, and how you can use it in your applications.
Luminoth: AI Powered Computer Vision for Python with Joaquin Alori
Making computers identify and understand what they are looking at in digital images is an ongoing challenge. Recent years have seen notable increases in the accuracy and speed of object detection due to deep learning and new applications of neural networks. In order to make it easier for developers to take advantage of these techniques Tryo Labs built Luminoth. In this interview Joaquin Alori explains how how Luminoth works, how it can be used in your projects, and how it compares to API oriented services for computer vision.
Thonny: The IDE For Beginning Programmers with Aivar Annamaa
Learning to program is a rewarding pursuit, but is often challenging. One of the roadblocks on the way to proficiency is getting a development environment installed and configured. In order to simplify that process Aivar Annamaa built Thonny, a Python IDE designed for beginning programmers. In this episode he discusses his initial motivations for starting Thonny and how it helps newcomers to Python learn and understand how to write software.
Keeping The Beets with Adrian Sampson
Maintaining a consistent taxonomy for your music library is a challenging and time consuming endeavor. Eventually you end up with a mess of folders and files with inconsistent names and missing metadata. Beets is built to solve this problem by programmatically managing the tags and directory structure for all of your music files and providing a fast lookup when you are trying to find that perfect song to play. Adrian Sampson began the project because he was trying to clean up his own music collection and in this episode he discusses how the project was built, how streaming media is affecting our relationship to digital music, and how he envisions Beets position in the ecosystem in the future.
Salabim: Logistics Simulation with Ruud van der Ham
Determining the best way to manage the capacity and flow of goods through a system is a complicated issue and can be exceedingly expensive to get wrong. Rather than experimenting with the physical objects to determine the optimal algorithm for managing the logistics of everything from global shipping lanes to your local bank, it is better to do that analysis in a simulation. Ruud van der Ham has been working in this area for the majority of his professional life at the Dutch port of Rotterdam. Using his acquired domain knowledge he wrote Salabim as a library to assist others in writing detailed simulations of their own and make logistical analysis of real world systems accessible to anyone with a Python interpreter.
Laboratory: Safer Refactoring with Joe Alcorn
Every piece of software that has been around long enough ends up with some piece of it that needs to be redesigned and refactored. Often the code that needs to be updated is part of the critical path through the system, increasing the risks associated with any change. One way around this problem is to compare the results of the new code against the existing logic to ensure that you aren't introducing regressions. This week Joe Alcorn shares his work on Laboratory, how the engineers at GitHub inspired him to create it as an analog to the Scientist gem, and how he is using it for his day job.
Software Architecture For Developers with Neal Ford
Whether it is intentional or accidental, every piece of software has an existing architecture. In this episode Neal Ford discusses the role of a software architect, methods for improving the design of your projects, pitfalls to avoid, and provides some resources for continuing to learn about how to design and build successful systems.