Talk Julia

Visual Studio Code (VS Code) is a popular code editor from Microsoft, and the Julia VS Code extension makes working with Julia in VS Code a real treat. Learn how to install Julia, VS Code, the Julia VS Code extension, and how to use the Julia VS Code extension features to create a robust and fluid Julia development environment.

We're excited to announce that we have opened up podcast memberships! Become a member for as little as $5/mo to get early access to episodes, social media shoutouts, and, starting at $10/mo and up, access to a members-only "office hours" call.

Your support helps us continue to bring you interviews and educational Julia content each week. It also helps us grow sustainably and improve the quality of the podcast.

Become a member today 👉

We've partnered with Manning Publications to bring all of our listeners a special 35% discount code on all of Mannings physical books, ebooks, courses, and more. There's no expiration date and you can use the discount as many times as you like!

Just visit http://mng.bz/pOzw and use the code podtalkjulia22 at checkout to get 35% off of your order!

Links:

• Julia Download Page
• Visual Studio Code website
• Julia VS Code extension website
• Julia VS Code extension docs
• JuliaMono website
• JuliaMono GitHub repo

David and Randy explore deep neural networks in Julia using Flux.jl by recreating Grant Sanderson's model for predicting handwritten digits in the MNIST data set. We also show how to visualize model results and training performance in TensorBoard using the TensorBoardLogging.jl package.

We're excited to announce that we have opened up podcast memberships! Become a member for as little as $5/mo to get early access to episodes, social media shoutouts, and, starting at $10/mo and up, access to a members-only "office hours" call.

Your support helps us continue to bring you interviews and educational Julia content each week. It also helps us grow sustainably and improve the quality of the podcast.

Become a member today 👉

We've partnered with Manning Publications to bring all of our listeners a special 35% discount code on all of Mannings physical books, ebooks, courses, and more. There's no expiration date and you can use the discount as many times as you like!

Just visit http://mng.bz/pOzw and use the code podtalkjulia22 at checkout to get 35% off of your order!

Links:

• Grant Sanderson's (3blue1brown) Neural networks playlist (YouTube)
• Neural Network and Deep Learning (Free online book)
• Flux.jl Website
• TensorBoard website
• TensorBoardLogger.jl (GitHub)
• TensorBoardLogger.jl (Docs)
• Code for this episode

Flux.jl is Julia's elegant machine learning library, but its API is a little different than Tensorflow or PyTorch. This week on Talk Julia, David and Randy dive into Flux.jl, explore some of the big differences between Flux and Python's machine learning libraries, and offer up some tips and tricks for learning Flux if you're coming to it from another ecosystem.

We're excited to announce that we have opened up podcast memberships! Become a member for as little as $5/mo to get early access to episodes, social media shoutouts, and, starting at $10/mo and up, access to a members-only "office hours" call.

Your support helps us continue to bring you interviews and educational Julia content each week. It also helps us grow sustainably and improve the quality of the podcast.

Become a member today 👉

We've partnered with Manning Publications to bring all of our listeners a special 35% discount code on all of Mannings physical books, ebooks, courses, and more. There's no expiration date and you can use the discount as many times as you like!

Just visit http://mng.bz/pOzw and use the code podtalkjulia22 at checkout to get 35% off of your order!

Links:

• Flux.jl GitHub
• Flux.jl Website
• Quick Overview of Flux.jl
• Randy's Flux notebooks
• Flux <3 NumFOCUS — We are very excited to announce that FluxML is partnering with NumFOCUS as an affiliated project to further the cause of open and reproducible science and growing the adoption of the FluxML ecosystem... This partnership will help us in growing the community, bringing new contributors into the ecosystem, and help us manage the funds from future grants we raise for a number of upcoming projects.

David and Randy take a look at reinforcement learning in Julia by diving into the ReinforcementLearning.jl package. We talk a little bit about what reinforcement learning is, as well as our thoughts on ReinforcementLearning.jl's design, which taps into Julia's multiple dispatch system.

We're excited to announce that we have opened up podcast memberships! Become a member for as little as $5/mo to get early access to episodes, social media shoutouts, and, starting at $10/mo and up, access to a members-only "office hours" call.

Your support helps us continue to bring you interviews and educational Julia content each week. It also helps us grow sustainably and improve the quality of the podcast.

Become a member today 👉

We've partnered with Manning Publications to bring all of our listeners a special 35% discount code on all of Mannings physical books, ebooks, courses, and more. There's no expiration date and you can use the discount as many times as you like!

Just visit http://mng.bz/pOzw and use the code podtalkjulia22 at checkout to get 35% off of your order!

Links:

• Why We Use Julia, 10 Years Later — Exactly ten years ago today, we published "Why We Created Julia", introducing the Julia project to the world. At this point, we have moved well past the ambitious goals set out in the original blog post. Julia is now used by hundreds of thousands of people. It is taught at hundreds of universities and entire companies are being formed that build their software stacks on Julia.
• AlphaGo Documentary (YouTube)
• An Introduction to ReinforcementLearning.jl: Design, Implementations and Thoughts — The goal of ReinforcementLearning.jl is to provide a collection of tools for learning and implementing reinforcement learning algorithms. This means that, unlike many other existing packages focusing on deep reinforcement learning only, we aim to build a rich ecosystem to solve different kinds of tasks in reinforcement learning.
• ReinforcementLearning.jl Docs
• JuliaReinforcementLearning GitHub Organization
• JuliaCon 2022 Website

This week we asked Julia Twitter what their favorite Julia packages are, and the responses don't disappoint! We picked two packages to investigate this week: Randy dives into DataFrames.jl and David explores the amazing Revise.jl package.

Randy also shares an example of building a single neuron machine learning model from scratch in Julia, and David talks about some animations he made this week using Javis.jl.

We're excited to announce that we have opened up podcast memberships! Become a member for as little as $5/mo to get early access to episodes, social media shoutouts, and, starting at $10/mo and up, access to a members-only "office hours" call.

Your support helps us continue to bring you interviews and educational Julia content each week. It also helps us grow sustainably and improve the quality of the podcast.

Become a member today 👉

We've partnered with Manning Publications to bring all of our listeners a special 35% discount code on all of Mannings physical books, ebooks, courses, and more. There's no expiration date and you can use the discount as many times as you like!

Just visit http://mng.bz/pOzw and use the code podtalkjulia22 at checkout to get 35% off of your order!

Links:

• Twitter thread with everyone's favorite packages
• DataFrames.jl Docs
• Revise.jl Docs
• Randy's Talk Julia GitHub
• Javis.jl Docs
• Gist for the circle illusion animation
• Gist for the pulsating circles animation

David and Randy explore graph theory with Juli. We learn how to use the Graphs.jl Julia package to create graph data structures, and the JuMP.jl package to calculate NP-hard and NP-complete properties of the graphs.

This week we also share a bunch of amazing things that our listeners are building in Julia. Thanks to everyone that shared your projects with us! Check out the Twitter thread to see what everyone is working on.

We're excited to announce that we have opened up podcast memberships! Become a member for as little as $5/mo to get early access to episodes, social media shoutouts, and, starting at $10/mo and up, access to a members-only "office hours" call.

Your support helps us continue to bring you interviews and educational Julia content each week. It also helps us grow sustainably and improve the quality of the podcast.

Become a member today 👉

We've partnered with Manning Publications to bring all of our listeners a special 35% discount code on all of Mannings physical books, ebooks, courses, and more. There's no expiration date and you can use the discount as many times as you like!

Just visit http://mng.bz/pOzw and use the code podtalkjulia22 at checkout to get 35% off of your order!

Links:

• Twitter thread showcasing listener projects
• HeartBeats.jl by Clemens Brunner
• Graphs.jl GitHub
• Graphs.jl Documentation
• JuMP.jl GitHub
• JuMP.jl Documentation
• Hearing the Shape of a Graph by Rob Ellis
• The Music of Graphs by Jason Davies
• David's script for generating scales and chords from graphs
• David's script for generating a graphs chord and an image of the graph
• GitHub issue explaining how to use NormalizedLaplacian in Graph.jl
• Julia Command-line Options (Julia Docs)
• JULIA_PROJECT Environment Variable (Julia Docs)

David and Randy share some of their thoughts on functions, methods, and multiple dispatch in Julia from the perspective as new users coming to Julia from Python.

Learn how Julia's type declarations and multiple dispatch system allow you to redefine functions with different behavior depending on the types of their arguments and explore some of the differences between functions in Julia and functions in Python.

We're excited to announce that we have opened up podcast memberships! Become a member for as little as $5/mo to get early access to episodes, social media shoutouts, and, starting at $10/mo and up, access to a members-only "office hours" call.

Your support helps us continue to bring you interviews and educational Julia content each week. It also helps us grow sustainably and improve the quality of the podcast.

Become a member today 👉

We've partnered with Manning Publications to bring all of our listeners a special 35% discount code on all of Mannings physical books, ebooks, courses, and more. There's no expiration date and you can use the discount as many times as you like!

Just visit http://mng.bz/pOzw and use the code podtalkjulia22 at checkout to get 35% off of your order!

Links:

• Functions (Julia Manual)
• LinearAlgebra bitarray.jl (Julia GitHub)
• Style Guide (Julia Manual)
• Methods (Julia Manual)
• The Unreasonable Effectiveness of Multiple Dispatch (Stefan Karpinski)
• Types (Julia Manual)

David and Randy explore Julia packages for reading, writing, and manipulating music and MIDI files, discuss whether or not Julia should be taught as the primary language in a machine learning course, an amazing Julia resource for Pythonistas, and a whole lot more.

We're excited to announce that we have opened up podcast memberships! Become a member for as little as $5/mo to get early access to episodes, social media shoutouts, and, starting at $10/mo and up, access to a members-only "office hours" call.

Your support helps us continue to bring you interviews and educational Julia content each week. It also helps us grow sustainably and improve the quality of the podcast.

Become a member today 👉

We've partnered with Manning Publications to bring all of our listeners a special 35% discount code on all of Mannings physical books, ebooks, courses, and more. There's no expiration date and you can use the discount as many times as you like!

Just visit http://mng.bz/pOzw and use the code podtalkjulia22 at checkout to get 35% off of your order!

Links:

• Hey Julia song by PianoHamster
• JuliaMusic.jl GitHub
• JuliaMusic.jl docs
• How To Get a Job Programming In Julia by Logan Kilpatrick
• Julia for Pythonistas Google Colab Notebook by Aurélien Geron
• juliabloggers.com
• The History of Programming DevCon 2016 Talk by Mark Rendle
• Why, how, and when of ∘ by Bogumił Kamiński

This week on Talk Julia, David and Randy discuss the a cuter alternative to the Iris dataset for teaching machine learning: PalmerPenguin.jl. We also dive into some awesome features of Julia's package manager — namely stacked environments — an awesome You Tube channel for learning Julia, and what the difference between using and import is when importing a package into your project.

We're excited to announce that we have opened up podcast memberships! Become a member for as little as $5/mo to get early access to episodes, social media shoutouts, and, starting at $10/mo and up, access to a members-only "office hours" call.

Your support helps us continue to bring you interviews and educational Julia content each week. It also helps us grow sustainably and improve the quality of the podcast.

Become a member today 👉

We've partnered with Manning Publications to bring all of our listeners a special 35% discount code on all of Mannings physical books, ebooks, courses, and more. There's no expiration date and you can use the discount as many times as you like!

Just visit http://mng.bz/pOzw and use the code podtalkjulia22 at checkout to get 35% off of your order!

Links:

• Julia in the classroom
• PalmerPenguins.jl GitHub Repository
• The Most Underrated Feature of the Julia Language, the Package Manager by Logan Kilpatrick
• Working With Environments (Julia Docs)
• Environment Stacks (Julia Docs)
• JULIA_LOAD_PATH Environment Variable (Julia Docs)
• Julia For Talented Amateurs (YouTube)
• Using and Import (Julia Docs)

Are you you thinking about learning the Julia programming language and you're already fluent in another language, such as R, Python, or MATLAB? In this week's episode, we explore tips, ticks, and resources to help you get up to speed with the Julia language as quickly as possible.

We're excited to announce that we have opened up podcast memberships! Become a member for as little as $5/mo to get early access to episodes, social media shoutouts, and, starting at $10/mo and up, access to a members-only "office hours" call.

Your support helps us continue to bring you interviews and educational Julia content each week. It also helps us grow sustainably and improve the quality of the podcast.

Become a member today 👉

We've partnered with Manning Publications to bring all of our listeners a special 35% discount code on all of Mannings physical books, ebooks, courses, and more. There's no expiration date and you can use the discount as many times as you like!

Just visit http://mng.bz/pOzw and use the code podtalkjulia22 at checkout to get 35% off of your order!

Links:

• 10 Things I Love About Julia by Trang Le
• The Julia REPL (Julia Docs)
• Learn Julia For Beginners – The Future Programming Language of Data Science and Machine Learning Explained by Logan Kilpatrick
• Julia Tutorial by Derek Banas
• Noteworthy Differences From Other Languages (Julia Docs)
• MATLAB-Python-Julia Cheatsheet by QuantEcon
• Plots.jl Package Documentation