Datadog

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

Thonny: The IDE For Beginning Programmers with Aivar Annamaa - Episode 153

Summary

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.

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 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 podcastinit.com to subscribe to the show, sign up for the newsletter, and read the show notes.
  • Your host as usual is Tobias Macey and today I’m interviewing Aivar Annamaa about Thonny, a Python IDE for beginning programmers

Interview

  • Introductions
  • How did you get introduced to Python?
  • What was your motivation for building an IDE focused on beginning programmers?
  • What are the features of Thonny that make it easier for users to understand what is happening in their programs?
  • What have you found to be the types of issues that users most frequently struggle with and how does Thonny help overcome those gaps in understanding?
  • What kinds of tutorials or supporting material have you found to be the most useful for teaching students the principles that they need to be able to take advantage of the environment that Thonny provides?
  • How is Thonny built and what have been the most challenging aspects of writing an IDE in Python?
  • What are some of the interface design choices that you have made to avoid confusing or overwhelming beginning users?
  • Once a user becomes more proficient in Python is there a point where it no longer makes sense to continue using Thonny for development?
  • I noticed that Thonny has an plugin architecture and there is an extension for interacting with the BBC micro:bit. What are some of the other types of extensions that you would like to see built for Thonny?

Keep In Touch

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