The weekly podcast about Python and its use in machine learning and data science. Tune in for engaging, educational, and technical discussions about machine learning, artificial intelligence, data science, and scientific research that is powered by Python.
An Exploration Of Financial Exchange Risk Management Strategies
The world of finance has driven the development of many sophisticated techniques for data analysis. In this episode Paul Stafford shares his experiences working in the realm of risk management for financial exchanges. He discusses the types of risk that are involved, the statistical methods that he has found most useful for identifying strategies to mitigate that risk, and the software libraries that have helped him most in his work.
Build Better Machine Learning Models By Understanding Their Decisions With SHAP
Machine learning and deep learning techniques are powerful tools for a large and growing number of applications. Unfortunately, it is difficult or impossible to understand the reasons for the answers that they give to the questions they are asked. In order to help shine some light on what information is being used to provide the outputs to your machine learning models Scott Lundberg created the SHAP project. In this episode he explains how it can be used to provide insight into which features are most impactful when generating an output, and how that insight can be applied to make more useful and informed design choices. This is a fascinating and important subject and this episode is an excellent exploration of how to start addressing the challenge of explainability.
Accelerating Drug Discovery Using Machine Learning With TorchDrug
Finding new and effective treatments for disease is a complex and time consuming endeavor, requiring a high degree of domain knowledge and specialized equipment. Combining his expertise in machine learning and graph algorithms with is interest in drug discovery Jian Tang created the TorchDrug project to help reduce the amount of time needed to find new candidate molecules for testing. In this episode he explains how the project is being used by machine learning researchers and biochemists to collaborate on finding effective treatments for real-world diseases.
An Exploration Of Automated Speech Recognition
The overwhelming growth of smartphones, smart speakers, and spoken word content has corresponded with increasingly sophisticated machine learning models for recognizing speech content in audio data. Dylan Fox founded Assembly to provide access to the most advanced automated speech recognition models for developers to incorporate into their own products. In this episode he gives an overview of the current state of the art for automated speech recognition, the varying requirements for accuracy and speed of models depending on the context in which they are used, and what is required to build a special purpose model for your own ASR applications.
Experimenting With Reinforcement Learning Using MushroomRL
Reinforcement learning is a branch of machine learning and AI that has a lot of promise for applications that need to evolve with changes to their inputs. To support the research happening in the field, including applications for robotics, Carlo D'Eramo and Davide Tateo created MushroomRL. In this episode they share how they have designed the project to be easy to work with, so that students can use it in their study, as well as extensible so that it can be used by businesses and industry professionals. They also discuss the strengths of reinforcement learning, how to design problems that can leverage its capabilities, and how to get started with MushroomRL for your own work.
Doing Dask Powered Data Science In The Saturn Cloud
A perennial problem of doing data science is that it works great on your laptop, until it doesn't. Another problem is being able to recreate your environment to collaborate on a problem with colleagues. Saturn Cloud aims to help with both of those problems by providing an easy to use platform for creating reproducible environments that you can use to build data science workflows and scale them easily with a managed Dask service. In this episode Julia Signall, head of open source at Saturn Cloud, explains how she is working with the product team and PyData community to reduce the points of friction that data scientists encounter as they are getting their work done.
Goes Deep in a Good Way
I enjoy some episodes of other Python podcasts, but I enjoy every episode of this podcast. Tobias’ expertise and experience allow for a level of depth that makes his podcast stand out.
Love it, great well informed questions.
At first it was hard to get into the podcasts, now it’s one I look forward too. He asks really good questions. It’s easy to tell Tobus does his homework. The only suggestion I’d give is to pause for an answer between each question. Every question seems to be two distinct questions for the guests. Maybe it is so the guests can speak to what they are most comfortable, but each of the questions are great and would be good on their own. Overall, I would highly recommend to anyone with a technical bent and especially a pythonic one.
Starts hard but hang in there
I agree that the delivery is a little flat but this guy asks really thoughtful questions. I’ve been listening for over a year and like I said, keep listening. Watch your guests levels though. If the levels are too off, I move on.