Machine Learning

Electricity Map: Real Time Visibility of Power Generation with Olivier Corradi - Episode 157

Summary

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.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • Finding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.
  • To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • Your host as usual is Tobias Macey and today I’m interviewing Olivier Corradi about Electricity Map and using Python to analyze data of global power generation

Interview

  • Introductions
  • How did you get introduced to Python?
  • What was your motivation for creating Electricity Map?
    • How can an average person use or benefit from the information that is available in the map?
  • What sources are you using to gather the information about how electricity is generated and distributed in various geographic regions?
    • Is there any standard format in which this data is produced?
    • What are the biggest difficulties associated with collecting and consuming this data?
    • How much confidence do you have in the accuracy of the data sources?
    • Is there any penalty for misrepresenting the fuel consumption or waste generation for a given plant?
  • Can you describe the architecture of the system and how it has evolved?
  • What are some of the most interesting uses of the data in your database and API that you are aware of?
    • How do you measure the impact or effectiveness of the information that you provide through the different interfaces to the data that you have aggregated?
  • How have you built a community around the project?
    • How has the community helped in building and growing Electricity Map?
  • What are some of the most unexpected things that you have learned in the process of building Electricity Map?
  • What are your plans for the future of Electricity Map?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Luminoth: AI Powered Computer Vision for Python with Joaquin Alori - Episode 154

Summary

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 Joaquín 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.

Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • For complete visibility into your application stack, deployment tracking, and powerful alerting, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix bugs in no time. Go to podcastinit.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt.
  • To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • Your host as usual is Tobias Macey and today I’m interviewing Joaquín Alori about Luminoth, a deep learning toolkit for computer vision in Python

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is Luminoth and what was your motivation for creating it?
  • Computer vision has been a focus of AI research for decades. How do current approaches with deep learning compare to previous generations of tooling?
  • What are some of the most difficult problems in visual processing that still need to be solved?
  • What are the limitations of Luminoth for building a computer vision application and how do they differ from the capabilities of something built with a prior generation of tooling such as OpenCV?
  • For someone who is interested in using Luminoth in their project what is the current workflow?
  • How do the capabilities of Luminoth compare with some of the various service based options such as Rekognition for Amazon or the Cloud Vision API from Google?
    • What are some of the motivations for using Luminoth in place of these services?
  • What are some of the highest priority features that you are focusing on implementing in Luminoth?
  • When is Luminoth the wrong choice for a computer vision application and what are some of the strongest alternatives at the moment?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Learn Leap Fly: Using Python To Promote Global Literacy with Kjell Wooding - Episode 145

Summary

Learning how to read is one of the most important steps in empowering someone to build a successful future. In developing nations, access to teachers and classrooms is not universally available so the Global Learning XPRIZE serves to incentivize the creation of technology that provides children with the tools necessary to teach themselves literacy. Kjell Wooding helped create Learn Leap Fly in order to participate in the competition and used Python and Kivy to build a platform for children to develop their reading skills in a fun and engaging environment. In this episode he discusses his experience participating in the XPRIZE competition, how he and his team built what is now Kasuku Stories, and how Python and its ecosystem helped make it possible.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Kjell Wooding about Learn Leap Fly, a startup using Python on mobile devices to facilitate global learning

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by describing what Learn Leap Fly does and how the company got started?
  • What was your motivation for using Kivy as the primary technology for your mobile applications as opposed to the platform native toolkits or other multi-platform frameworks?
  • What are some of the pedagogical techniques that you have incorporated into the technological aspects of your mobile application and are there any that you were unable to translate to a purely technical implementation.
  • How do you measure the effectiveness of the work that you are doing?
  • How has the framework of the XPRIZE influenced the way in which you have approached the design and development of your work?
  • What have been some of the biggest challenges that you faced in the process of developing and deploying your submission for the XPRIZE?
  • What are some of the features that you have planned for future releases of your platform?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Orange: Visual Data Mining Toolkit with Janez Demšar and Blaž Zupan - Episode 142

Summary

Data mining and visualization are important skills to have in the modern era, regardless of your job responsibilities. In order to make it easier to learn and use these techniques and technologies Blaž Zupan and Janez Demšar, along with many others, have created Orange. In this episode they explain how they built a visual programming interface for creating data analysis and machine learning workflows to simplify the work of gaining insights from the myriad data sources that are available. They discuss the history of the project, how it is built, the challenges that they have faced, and how they plan on growing and improving it in the future.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Blaž Zupan and Janez Demsar about Orange, a toolbox for interactive machine learning and data visualization in Python

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is Orange and what was your motivation for building it?
  • Who is the target audience for this project?
  • How is the graphical interface implemented and what kinds of workflows can be implemented with the visual components?
  • What are some of the most notable or interesting widgets that are available in the catalog?
  • What are the limitations of the graphical interface and what options do user have when they reach those limits?
  • What have been some of the most challenging aspects of building and maintaining Orange?
  • What are some of the most common difficulties that you have seen when users are just getting started with data analysis and machine learning, and how does Orange help overcome those gaps in understanding?
  • What are some of the most interesting or innovative uses of Orange that you are aware of?
  • What are some of the projects or technologies that you consider to be your competition?
  • Under what circumstances would you advise against using Orange?
  • What are some widgets that you would like to see in future versions?
  • What do you have planned for future releases of Orange?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Surprise! Recommendation Algorithms with Nicolas Hug - Episode 135

Summary

A relevant and timely recommendation can be a pleasant surprise that will delight your users. Unfortunately it can be difficult to build a system that will produce useful suggestions, which is why this week’s guest, Nicolas Hug, built a library to help with developing and testing collaborative recommendation algorithms. He explains how he took the code he wrote for his PhD thesis and cleaned it up to release as an open source library and his plans for future development on it.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Nicolas Hug about Surprise, a scikit library for building recommender systems

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is Surprise and what was your motivation for creating it?
  • What are the most challenging aspects of building a recommender system and how does Surprise help simplify that process?
  • What are some of the ways that a user or company can bootstrap a recommender system while they accrue data to use a collaborative algorithm?
  • What are some of the ways that a recommender system can be used, outside of the typical ecommerce example?
  • Once an algorithm has been deployed how can a user test the accuracy of the suggestions?
  • How is Surprise implemented and how has it evolved since you first started working on it?
  • What have been the most difficult aspects of building and maintaining Surprise?
  • competitors?
  • What are the attributes of the system that can be modified to improve the relevance of the recommendations that are provided?
  • For someone who wants to use Surprise in their application, what are the steps involved?
  • What are some of the new features or improvements that you have planned for the future of Surprise?

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  • Tobias
    • Silk profiler for Django

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Rasa: Build Your Own AI Chatbot with Joey Faulkner - Episode 134

Summary

With the proliferation of messaging applications, there has been a growing demand for bots that can understand our wishes and perform our bidding. The rise of artificial intelligence has brought the capacity for understanding human language. Combining these two trends gives us chatbots that can be used as a new interface to the software and services that we depend on. This week Joey Faulkner shares his work with Rasa Technologies and their open sourced libraries for understanding natural language and how to conduct a conversation. We talked about how the Rasa Core and Rasa NLU libraries work and how you can use them to replace your dependence on API services and own your data.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Joey Faulkner about Rasa Core and Rasa NLU for adding conversational AI to your projects.

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining the goals of Rasa as a company and highlighting the projects that you have open sourced?
  • What are the differences between the Rasa Core and Rasa NLU libraries and how do they relate to each other?
  • How does the interaction model change when going from state machine driven bots to those which use Rasa Core and what capabilities does it unlock?
  • How is Rasa NLU implemented and how has the design evolved?
  • What are the motivations for someone to use Rasa core or NLU as a library instead of available API services such as wit.ai, LUIS, or Dialogflow?
  • What are some of the biggest challenges in gathering and curating useful training data?
  • What is involved in supporting multiple languages for an application using Rasa?
  • What are the biggest challenges that you face, past, present, and future, building and growing the tools and platform for Rasa?
  • What would be involved for projects such as OpsDroid, Kalliope, or Mycroft to take advantage of Rasa and what benefit would that provide?
  • On the comparison page for the hosted Rasa platform it mentions a feature of collaborative model training, can you describe how that works and why someone might want to take advantage of it?
  • What are some of the most interesting or unexpected uses of the Rasa tools that you have seen?
  • What do you have planned for the future of Rasa?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Donkey: Building Self Driving Cars with Will Roscoe - Episode 132

Summary

Do you wish that you had a self-driving car of your own? With Donkey you can make that dream a reality. This week Will Roscoe shares the story of how he got involved in the arena of self-driving car hobbyists and ended up building a Python library to act as his pilot. We talked about the hardware involved, how he has evolved the code to meet unexpected challenges, and how he plans to improve it in the future. So go build your own self driving car and take it for a spin!

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Will Roscoe about Donkey, a python library for building DIY self driving cars.

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is Donkey and what was your reason for creating it?
    • What is the story behind the name?
  • What was your reason for choosing Python as the language for implementing Donkey and if you were to start over today would you make the same choice?
  • How is Donkey implemented and how has its software architecture evolved?
  • Is the library built in a way that you can process inputs from additional sensor types, such as proximity detectors or LIDAR?
  • For training the autopilot what are the input features that the model is testing against for the input data, and is it possible to change the features that it will try to detect?
  • Do you have plans to incorporate any negative reinforcement techniques for training the pilot models so that errors in data collection can be identified as undesirable outcomes?
  • What have been some of the most interesting or humorous successes and failures while testing your cars?
  • What are some of the challenges involved with getting such a sophisticated stack of software running on a Raspberry Pi?
  • What are some of the improvements or new features that you have planned for the future of Donkey?

Media

Donkey Car Photos

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA