The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

October 22, 2019Illustrating The Landscape And Applications Of Deep Learning
Summary
Deep learning is a phrase that is used more often as it continues to transform the standard approach to artificial intelligence and machine learning projects. Despite its ubiquity, it is often difficult to get a firm understanding of how it works and how it can be applied to a particular problem. In this episode Jon Krohn, author of Deep Learning Illustrated, shares the general concepts and useful applications of this technique, as well as sharing some of his practical experience in using it for his work. This is definitely a helpful episode for getting a better comprehension of the field of deep learning and when to reach for it in your own projects.
Announcements
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 or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
Your host as usual is Tobias Macey and today I’m interviewing Jon Krohn about his recent book, deep learning illustrated
Interview
Introductions
How did you get introduced to Python?
Can you start by giving a brief description of what we’re talking about when we say deep learning and how you got involved with the field?
How does your background in neuroscience factor into your work on designing and building deep learning models?
What are some of the ways that you leverage deep learning techniques in your work?
What was your motivation for writing a book on the subject?
How did the idea of including illustrations come about and what benefit do they provide as compared to other books on this topic?
While planning the contents of the book what was your thought process for determining the appropriate level of depth to cover?
How would you characterize the target audience and what level of familiarity and proficiency in employing deep learning do you wish them to have at the end of the book?
How did you determine what to include and what to leave out of the book?
The sequencing of the book follows a useful progression from general background to specific uses and problem domains. What were some of the biggest challenges in determining which domains to highlight and how deep in each subtopic to go?
Because of the continually evolving nature of the field of deep learning and the associated tools, how have you guarded against obsolescence in the content and structure of the book?
Which libraries did you focus on for your examples and what was your selection process?
Now that it is published, is there anything that you would have done differently?
One of the critiques of deep learning is that the models are generally single purpose. How much flexibility and code reuse is possible when trying to repurpose one model pipeline for a slightly different dataset or use case?
I understand that deployment and maintenance of models in production environments is also difficult. What has been your experience in that regard, and what recommendations do you have for practitioners to reduce their complexity?
What is involved in actually creating and using a deep learning model?
Can you go over the different types of neurons and the decision making that is required when selecting the network topology?
In terms of the actual development process, what are some useful practices for organizing the code and data that goes into a model, given the need for iterative experimentation to achieve desired levels of accuracy?
What is your personal workflow when building and testing a new model for a new use case?
What are some of the limitations of deep learning and cases where you would recommend against using it?
What are you most excited for in the field of deep learning and its applications?
What are you most concerned by?
Do you have any parting words or closing advice for listeners and potential readers?
Keep In Touch
Website
@jonkrohnlearns on Twitter
jonkrohn on GitHub
Picks
Tobias
Spurious Correlations
Jon
Data Elixir Newsletter
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Links
Untapt
Deep Learning Illustrated
Pearson
Columbia University
New York City Data Science Academy
NIH (National Institutes of Health)
Oxford Uniersity
Matlab
R Language
Neuroscience
Artificial Neural Network
Deep Learning
Natural Language Processing
Computer Vision
Generative Adversarial Networks
Deep Learning by Ian Goodfellow, et al.
Hands On Machine Learning by Aurélien Géron
O’Reilly Online Learning
Transfer Learning
Keras
Tensorflow
PyTorch
Gary Marcus
Judea Pearl
Artificial General Intelligence
Explainable AI
Yuval Noah Harrari
Sapiens
Home Deus
Wait But Why?
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
57 min
October 14, 2019 Andrew's Adventures In Coderland
Software development is a unique profession in many ways, and it has given rise to its own subculture due to the unique sets of challenges that face developers. Andrew Smith is an author who is working on a book to share his experiences learning to program, and understand the impact that software is having on our world. In this episode he shares his thoughts on programmer culture, his experiences with Python and other language communities, and how learning to code has changed his views on the world. It was interesting getting an anthropological perspective from a relative newcomer to the world of software.
1 hr 1 min
October 14, 2019Andrew's Adventures In Coderland
Summary
Software development is a unique profession in many ways, and it has given rise to its own subculture due to the unique sets of challenges that face developers. Andrew Smith is an author who is working on a book to share his experiences learning to program, and understand the impact that software is having on our world. In this episode he shares his thoughts on programmer culture, his experiences with Python and other language communities, and how learning to code has changed his views on the world. It was interesting getting an anthropological perspective from a relative newcomer to the world of software.
Announcements
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 or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, Data Council in Barcelona, and the Data Orchestration Summit. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
Your host as usual is Tobias Macey and today I’m interviewing Andrew Smith about his anthropological study of software engineering culture in his upcoming book Adventures In Coderland.
Interview
Introductions
How did you get introduced to Python?
Can you start by describing the scope and intent of your work on Adventures In Coderland?
What was your motivation for embarking on this particular project?
Prior to the start of your research for this book, what was your level of familiarity with software development as a discipline and a cultural phenomenon?
How are you approaching the research for this book and to what level of detail are you trying to address the problem space?
What are some of the most striking contrasts that you have identified between software engineers and coding culture as it compares to that of a layperson?
We met at the most recent PyCon US, which I understand you attended as a means of conducting research for your book. What are some of the notable aspects of the Python community that you discovered while you were attending?
What are some of the other programming communities that you have engaged with?
What are some of the differentiating factors that you have noticed between the communities that you have interacted with?
What are some of the most surprising discoveries that you have made in the process of writing this book?
What is your metric for determining when you have gathered enough raw material to complete the book?
Now that you have delved into the peculiarities of "coderland", how has it changed your own outlook on both the software industry, and society at large?
What advice do you have for the engineers who are listening as it pertains to your experiences in writing your book?
Keep In Touch
Website
@wiresmith on Twitter
Picks
Tobias
Throughline Podcast
Andrew
20 Thousand Hertz Podcast
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Linksj
Adventures In Coderland
https://us.pycon.org?utm_source=rss&utm_medium=rss
Nicholas Tollervey
1843 Magazine
The Economist
Free Code Camp
Code Golf
Moon Dust book about the astronauts who first landed on the moon
The Face magazine
The Observer
The Guardian
Charlie Duke
Totally Wired
Code For America
Supercollider programming environment
SonicPi
George Boole
FMRI (Functional Magnetic Resonance Imaging)
Ruby Language
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
1 hr 1 min
October 08, 2019 Network Automation At Enterprise Scale With Python
Designing and maintaining enterprise networks and the associated hardware is a complex and time consuming task. Network automation tools allow network engineers to codify their workflows and make them repeatable. In this episode Antoine Fourmy describes his work on eNMS and how it can be used to automate enterprise grade networks. He explains how his background in telecom networking led him to build an open source platform for network engineers, how it is architected, and how you can use it for creating your own workflows. This is definitely worth listening to as a way to gain some appreciation for all of the work that goes on behind the scenes to make the internet possible.
35 min
October 08, 2019Network Automation At Enterprise Scale With Python
Summary
Designing and maintaining enterprise networks and the associated hardware is a complex and time consuming task. Network automation tools allow network engineers to codify their workflows and make them repeatable. In this episode Antoine Fourmy describes his work on eNMS and how it can be used to automate enterprise grade networks. He explains how his background in telecom networking led him to build an open source platform for network engineers, how it is architected, and how you can use it for creating your own workflows. This is definitely worth listening to as a way to gain some appreciation for all of the work that goes on behind the scenes to make the internet possible.
Announcements
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 or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
Your host as usual is Tobias Macey and today I’m interviewing Antoine Fourmy about eNMS, an enterprise-grade vendor-agnostic network automation platform.
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what eNMS is
What was your motivation for creating it?
Who are the target users of eNMS and how much background knowledge of network management is required to be effective with it?
What are some of the alternative tools that exist in this space and why might a network operator choose to use eNMS in their place?
What are some of the most challenging aspects of network creation and maintenance and how does eNMS assist with them?
What are some of the mundane and/or error-prone tasks that can be replaced or automated with eNMS?
What are some of the additional features that come into play for more complex networking tasks?
Can you describe the system architecture of eNMS and how it has evolved since you first began working on it?
eNMS is an impressive project that looks to have a substantial amount of polish. How large is the overall community of users and contributors?
For someone who wants to get involved in contributing to eNMS what are some of the types of skills and background that would be helpful?
What are some of the most innovative/unexpected ways that you have seen eNMS used?
When is eNMS the wrong choice?
What do you have planned for the future of the project?
Keep In Touch
Website
LinkedIn
afourmy on GitHub
Picks
Tobias
Tedeschi Trucks Band
Antoine
CheckIO
Podcast Episode
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Links
eNMS
Orange
Netmiko
NAPALM
Podcast Episode
Paramiko
Ansible
Requests
OpenNMS
LibreNMS
Ansible Tower
Rundeck
SaltStack
Podcast Episode
StackStorm
Podcast Episode
SaltStack Proxy Minions
Hashicorp Vault
VirtualBox
Flask
Django
SQLAlchemy
APScheduler
Docker
Podcast Episode
Redis
Celery
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
35 min
October 01, 2019 Building A Modern Discussion Forum In Python To Support Healthy Communities
Building and sustaining a healthy community requires a substantial amount of effort, especially online. The design and user experience of the digital space can impact the overall interactions of the participants and guide them toward respectful conversation. In this episode Rafał Pitoń shares his experience building the Misago platform for creating community forums. He explains his motivation for creating the project, the lessons he has learned in the process, and how it is being used by himself and others. This was a great conversation about how technology is just a means, and not the end in itself.
53 min
October 01, 2019Building A Modern Discussion Forum In Python To Support Healthy Communities
Summary
Building and sustaining a healthy community requires a substantial amount of effort, especially online. The design and user experience of the digital space can impact the overall interactions of the participants and guide them toward respectful conversation. In this episode Rafał Pitoń shares his experience building the Misago platform for creating community forums. He explains his motivation for creating the project, the lessons he has learned in the process, and how it is being used by himself and others. This was a great conversation about how technology is just a means, and not the end in itself.
Announcements
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 or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, Data Council in Barcelona, and the Data Orchestration Summit. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
Your host as usual is Tobias Macey and today I’m interviewing Rafał Pitoń about Misago, a fully featured modern forum application that is fast, scalable, and responsive
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what Misago is and your motivation for creating it?
How does it compare to other modern forum options such as Discourse and Flarum?
How did you generate and prioritize the set of features that you have implemented and what are the main capabilities that are still on your roadmap?
Is Misago intended to be run in isolation, or does it allow for integrating into a larger Django project?
Is there any support for multi-tenancy?
How is Misago itself implemented and how has the architecture evolved since you first began working on it?
If you were to start it today, what are some of the choices that you would make differently?
What are the extension points that developers can hook into for adding custom functionality?
In addition to the technical challenges, managing a forum involves a fair amount of social challenges. How does Misago help with management of a healthy community?
How do different design elements factor into promoting healthy conversation and sustainable engagement?
What are some of the aspects of community management and the accompanying platform features that enable them which aren’t initially obvious?
For someone who wants to use Misago, what is involved in deploying and configuring it?
What are some of the routine maintenance tasks that they should be aware of?
What are some of the most interesting or unexpected ways that you have seen Misago used?
What have you found to be the most interesting, unexpected, and challenging aspects of building and maintaining a forum platform?
What do you have planned for the future of Misago?
Keep In Touch
rafalp on GitHub
@RafalPiton on Twitter
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Picks
Tobias
Fear Innoculum by Tool
Rafał
github.com/encode
Ariadne GraphQL Library
Links
Misago
Poland
Mirumee
Saleor Episode
PHP
Discourse
Flarum
MySQL
PostgreSQL
Data Engineering Podcast Interview
jQuery
DJango Rest Framework
EmberJS
MithrilJS
AngularJS
ReactJS
PHPBB
Celery
GDPR == General Data Privacy Regulation
Docker
misago_docker
VPS == Virtual Private Server
Nginx
Starlette Async API framework
Ariadne GraphQL Library
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
53 min
September 23, 2019 Exploratory Data Analysis Made Easy At The Command Line
There are countless tools and libraries in Python for data scientists to perform powerful analyses, but they often have a setup cost that acts as a barrier to ad-hoc exploration of data. Visidata is a command line application that eliminates the friction involved with starting the discovery process. In this episode Saul Pwanson explains his motivation for creating it, why a terminal environment is a useful place for this work, and how you can use Visidata for your own work. If you have ever avoided looking at a data set because you couldn't be bothered with the boilerplate for a Jupyter notebook, then Visidata is the perfect addition to your toolbox.
53 min
September 23, 2019Exploratory Data Analysis Made Easy At The Command Line
Summary
There are countless tools and libraries in Python for data scientists to perform powerful analyses, but they often have a setup cost that acts as a barrier to ad-hoc exploration of data. Visidata is a command line application that eliminates the friction involved with starting the discovery process. In this episode Saul Pwanson explains his motivation for creating it, why a terminal environment is a useful place for this work, and how you can use Visidata for your own work. If you have ever avoided looking at a data set because you couldn’t be bothered with the boilerplate for a Jupyter notebook, then Visidata is the perfect addition to your toolbox.
Announcements
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 or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
Your host as usual is Tobias Macey and today I’m interviewing Saul Pwanson about Visidata, a terminal oriented interactive multitool for tabular data
Interview
Introductions
How did you get introduced to Python?
Can you start by describing what Visidata is and how the project got started?
What are the main use cases for Visidata?
What are some tools that it has replaced in your workflow?
Can you talk through a typical workflow for data exploration and analysis with Visidata?
One of the capabilities that you mention on the website is quickly opening large files. What are some strategies that you have used to enable performant access for files that might crash a typical editor (e.g. Vim, Emacs)?
Can you describe how Visidata is implemented and how it has evolved since you started working on it (including the upcoming 2.0 release)?
What libraries or language features have proven most useful?
Why did you choose to implement Visidata as a terminal only tool and what constraints does that bring with it?
What are some of the most challenging aspects of building a terminal UI for data exploration and analysis?
Because of its manifestation as a terminal/CLI application it relies heavily on keyboard bindings. How do you approach key assignments to ensure a consistent and intuitive user experience?
What are some of the types of analysis that Visidata can be used for out of the box?
What are some of the most interesting/unexpected/innovative ways that you have seen Visidata used?
How much community adoption have you seen and how do you approach project governance as a solo developer?
What do you have planned for the future of Visidata?
Keep In Touch
Website
saulpw on GitHub
@saulfp on Twitter
LinkedIn
Picks
Tobias
Data Is Plural newsletter
Saul
TMate
Mosh – The Mobile Shell
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Links
Visidata
F5 Networks
HDF5
PyTables
Podcast Interview
vgit
vping
Jeremy Singer-Vine
data.boston.gov
Recurse Center
Curses
dateutil
decorators
Electron
OpenRefine
Tmux
Visicalc
Windows Subsystem For Linux
Saul’s Lightning Talk
The Book of Visidata
Where In The World Is Carmen San Diego
Oh My Zsh
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
53 min
September 18, 2019 Cultivating The Python Community In Argentina
The Python community in Argentina is large and active, thanks largely to the motivated individuals who manage and organize it. In this episode Facundo Batista explains how he helped to found the Python user group for Argentina and the work that he does to make it accessible and welcoming. He discusses the challenges of encompassing such a large and distributed group, the types of events, resources, and projects that they build, and his own efforts to make information free and available. He is an impressive individual with a substantial list of accomplishments, as well as exhibiting the best of what the global Python community has to offer.
42 min