![PyCon Canada](/img/default-banner.jpg)
- 258
- 2 003 203
PyCon Canada
Canada
Приєднався 14 сер 2013
Відео
PyCon Canada 2019 Announcement: Come join us on November 16!
Переглядів 1,6 тис.4 роки тому
PyCon Canada 2019 is happening! Come join the largest gathering of Pythonistas in Canada on November 16 & 17 in Toronto, Ontario. Keynotes include: Ideshini Naidoo , Huda Idrees , Françoise Provencher and Will Lachance. More info at: 2019.pycon.ca Buy Tickets shop.pycon.ca/ Location: The Carlu, Toronto, Ontario Talks and Tutorials: November 16 - 17 Schedule 2019.pycon.ca/schedule-day-1/ Sprints...
Gathering Related Functionality: Patterns for Clean API Design (Paul Ganssle)
Переглядів 5745 років тому
This talk will arm you with some tools to design a library that 'just works', but also has obvious escape hatches to handle corner cases. It covers several patterns for cleanly organizing related and overlapping functionality in a way that statisfies both humans and static analysis tools. What do you do when you have to choose between designing your function for one of two common use cases? How...
The journey from mediocrity: how to stop feeling like a beginner (Victoria Mothersill)
Переглядів 7705 років тому
You read the docs, you did the learn to code exercises, you spent time in production. How do you know when you’re good at this? We’re programmers, so let’s break it up into parts. Let’s look at how we see ourselves, how our code performs, and how others see our code. Okay, now add, commit, push. Intro (1 minute): The python community is filled with many people from many disciplines that learned...
Open Sourcing at Work (Faisal Dosani)
Переглядів 3515 років тому
We just open sourced 2 projects (datacompy, and locopy) with roots in Data Science and Engineering which we will showcase. While is it exciting and rewarding to share your ideas with the world it isn't always easy. Thinking about licenses, copyrights, and protecting confidential information is a must! Working in a large organization which is embracing the mantra 'open source first' is really ex...
My code is not for you: Protecting Python developer’s identity in OSS (Alina Matyukhina)
Переглядів 1,7 тис.5 років тому
Full talk title: My code is not for you: Protecting Python developer’s identity in open-source software projects (OSS) OSS is open to anyone by design, whether it is developers or malicious users. Authors typically hide their identity through nicknames, however they have no protection against attribution techniques. This talk will present attacks on Python developers identity and discuss protec...
What a Bug can Teach You about Python (Brad Dettmer)
Переглядів 4735 років тому
We’ll take a look at some Python code that has a strange bug in it. You’ll learn why it’s a bug and why it only occurs with larger numbers. We’ll cover fixes, dive into how Python works and look at some CPython source code. You’ll learn about “is” vs “ ” and how to prevent bugs. We’ll take a look at some Python code that has a strange integer bug in it. You’ll learn about how the bug was discov...
API Evolution the Right Way (A. Jesse Jiryu Davis)
Переглядів 9405 років тому
Library maintainers, how can you innovate without breaking projects that depend on you? Follow semantic versioning, add APIs conservatively, add parameters compatibly, write an upgrade guide, use DeprecationWarnings, and publish a deprecation policy. Break backwards compatibility rarely and wisely. Staff Engineer at MongoDB in New York City specializing in C, Python, and async. Lead developer o...
How to Design and Build a Recommendation System Pipeline in Python (Jill Cates)
Переглядів 81 тис.5 років тому
Want to know how Spotify, Amazon, and Netflix generate recommendations for their users? This talk walks through the steps involved in building a recommendation pipeline, from data cleaning, hyperparameter tuning, model training and evaluation. Personalized recommendation systems play an integral role in e-commerce platforms, with the goal of driving user engagement. While there is extensive lit...
WSGI for Web Developers (Ryan Wilson-Perkin)
Переглядів 94 тис.5 років тому
WSGI is the foundation of most Python web frameworks, but there's a good chance you've never had to interact with it directly. In this talk we'll explore why it exists, how it works, and what the heck it's doing in your stack. A great web framework abstracts away all the low-level stuff so that you can focus on the core functionality of your application. This is helpful for getting you going qu...
Keynote talk (Solmaz Shahalizadeh)
Переглядів 5525 років тому
Solmaz is the VP of Data Science and Engineering at Shopify leading the data organization. Her teams build the data platform and the machine learning solutions that power Shopify's internal and merchant facing data products including Shopify's real-time Order Fraud Analysis, Shopify Capital and Shopify Home. Her and her teams build majority of their data solutions using Python (and Spark) and s...
Who's There? Building a home security system with Pi & Slack! (Ian Whitestone)
Переглядів 5605 років тому
How does one make use of that raspberry pi they bought years ago? This talk will summarize how you can turn your raspberry pi into a home security system, utilizing slack as a notifications and control system. Presentation page 2018.pycon.ca/talks/talk-PC-55476 Project page github.com/ian-whitestone/rpi-security-system
Why is Python ideal for research software development? (Pradeep Reddy Raamana)
Переглядів 5815 років тому
Python is showing an incredible growth in many fields, including academia. By enumerating the challenges we face in sustainable research software development and how Python's unique strengths are catering to them, I hope to explain this growth and encourage further adoption for scientific computing! Presentation page 2018.pycon.ca/talks/talk-PC-52179 Blog post on neuroinformatics crossinvalidat...
From Zero Code to Python Code (Nicole Parrot)
Переглядів 1,1 тис.5 років тому
Teaching computational thinking in the classroom is a challenge as there's a wide range of skills, including the teacher's. The Gigglebot is a microbit rover that covers the steps from no coding to Python coding through a variety of approaches so that no one in the classroom gets left behind. Presentation page 2018.pycon.ca/talks/talk-PC-54340 Author website eleanorstrib.com/
A Bossy Sort of Voice: Uncovering gender bias in Harry Potter with Python (Eleanor Stribling)
Переглядів 2575 років тому
Harry Potter is an incredibly popular franchise that shaped a generation, but it's also been critiqued for its biased portrayal of female characters. Does that claim hold up to a quantitative analysis? In this talk we'll use Python and Natural Language Processing techniques to find out. Presentation page 2018.pycon.ca/talks/talk-PC-55247 Code & blog post bit.ly/bossysortofvoice Author website e...
Software Design Simplified (Alex Tucker)
Переглядів 8695 років тому
Software Design Simplified (Alex Tucker)
The landscape of Quantum Computing in Python (Tomas Babej)
Переглядів 2,5 тис.5 років тому
The landscape of Quantum Computing in Python (Tomas Babej)
When Technical Debt Congeals: My Sabbatical with the Government of Canada (Jason White)
Переглядів 3025 років тому
When Technical Debt Congeals: My Sabbatical with the Government of Canada (Jason White)
Identifying influencers via Slack Messages in Python using Network Analysis and NLP (Eva Sasson)
Переглядів 1,4 тис.5 років тому
Identifying influencers via Slack Messages in Python using Network Analysis and NLP (Eva Sasson)
How our Python bot found your baseball ticket (Valérie Ouellet)
Переглядів 7185 років тому
How our Python bot found your baseball ticket (Valérie Ouellet)
Using Python to Quantify Portfolio Diversification (Robin Warner)
Переглядів 2,2 тис.5 років тому
Using Python to Quantify Portfolio Diversification (Robin Warner)
Using Python to detect malicious events at scale at Symantec Research Labs (Daniel Kats)
Переглядів 3405 років тому
Using Python to detect malicious events at scale at Symantec Research Labs (Daniel Kats)
The state of Open Source in Robotics, Cloud Computing, and Cancer Research (Daniel Snider)
Переглядів 1095 років тому
The state of Open Source in Robotics, Cloud Computing, and Cancer Research (Daniel Snider)
Building and scaling Deep Learning Services (Nischal Harohalli Padmanabha)
Переглядів 2085 років тому
Building and scaling Deep Learning Services (Nischal Harohalli Padmanabha)
Automate the Boring Stuff: Using Python to Improve University Courses (Swaleh Owais)
Переглядів 8485 років тому
Automate the Boring Stuff: Using Python to Improve University Courses (Swaleh Owais)
A Deep Learning Approach to Annotating de novo Transcriptome Assemblies (Matt Stata)
Переглядів 2165 років тому
A Deep Learning Approach to Annotating de novo Transcriptome Assemblies (Matt Stata)
Scaling multi-tenant apps using the Django ORM and Postgres (Sai Srirampur)
Переглядів 9 тис.5 років тому
Scaling multi-tenant apps using the Django ORM and Postgres (Sai Srirampur)
The Adventures of a Python Script! (Dema Abu Adas)
Переглядів 3705 років тому
The Adventures of a Python Script! (Dema Abu Adas)
How not to overfit your predictive models (Rebecca Tessier)
Переглядів 2705 років тому
How not to overfit your predictive models (Rebecca Tessier)
Excellent explanation. Loved it
How can can I contribute in your organisation
great
can you explain futher why the parameters of neutral and skeptical were halved?
Incredible presentation! I semi-disagree with precision and recall being good evaluation metrics for a recommendation system using a masking technique to evaluate model performance during the offline training phase. This is due to them demanding the output of the model to be binary, where as masked-prediction in this case would represent more of a regression problem leading RMSE to be a more valuable evaluation technique. Great presentation though, very clear explanations.
Still anyone using this!?
Yeah, I use it in a hobby project
Seems cool. I wish I could see the code he's talking about.
simply brilliant
I think this is the UA-cam video that covers GitHub deployment API specifically 🎉🙌🏽
I found this video while searching for more info on how to make noise in Python - I'm a hobbyist programmer looking to procedurally generate terrain for a 2D top-down game I'm currently working on. This helped me to understand the general way that noise is used to render terrain, so thank you very much 👍
What a fenomemal presenter!!! Geez...
Great introduction!
I’ve watched many recommendation engine videos and this is by far the best I’ve seen! Fantastic expertise and thought leadership.
Amazing. I don't see how any free content can be better than this. One of the best I ever seen.
Magnificent
Thanks for the explanation and example code on how the WSGI and web server are working together. The code demonstrate how they both work together step byt step in a very clear way.
I think my life has slightly changed to the better after watching this :D Thank you !
great session!!
Great presentation. Would you mind sharing the code? Thank you
Каеф!
Dude great explanation! 👍
Talk like others can understand Don't show up your communication skills!!
Very informative and an amazing session
It's so beautiful how you include those content in merely 20 mins! Well explained!
The analysis is good ... But the clipart SUCKS 👎
Great talk for a general overview on recommendation systems! From there I could deepen in the subjects I found interesting or didn't know about, in my opinion it's a great video for people with a general knowledge of ML or maybe that have some knowledge in other applications but never touched Recommendation Systems. Just one thing that doesn't come clear to me at the pre-processing part: When she talks about normalization, she talks about applying mean normalization for the users ratings, which comes clear, but the slides show a formula with "user-item rating bias" which she skips explaining, can someone explain me on where does the formula come from and if it's something that you should need to subtract from every cell? The fact that there is a variable for "global average" and another for "item's average rating" kinda confuses me, does the global average regards the whole dataset of movies? Thanks!
Suddenly matrix factorisation comes up. Why? What are its benefits and limitations. Ok i never studied this but it looks to me that im very dumb or the speaker jumps over a lot of issues.
thanks for this presentation
Nice presentation. Completely to the point.
Loved the presentation... The exact thing which i wanted... To get to the ROOT level of how Http Reqs are converted in my Django Views...🤔
Hello, is these code available online? Thanks
This talk was fantastic. Thank you
Great talk, watching 2nd time after 1 year.
Everything except ctypes are seems to be difficult to use.
great talk
Love it. Simple and to the point.
Awesome talk, there is just so much content on the web that tries to explain this topics but somehow end up missing the point entirely. The actual simple implementation/example is what helped me the most, thank you!
How do u make predictions bcz in knn for predictions we need train or test data by splitting but here we r using different approach for this so how gonna we make predictions for ds?
Amazing work! Its years of work and knowledge summarized in 24 mins (awesome flow)
Great presentation !
soooo awesome!!!
best explanation ever!
amazing amount of content in just 20 minutes! Also, thanks for covering train/test split- not everyone covers that with collaborative filtering.
Excellent presentation. Thank you.
Great talk. Thanks for sharing!
I learned it hard way! I went over Django and unicorn source code to understand it. But this is a gem. I wish I could have found this video earlier. Inspired from this talk I rebuild a WSGI server and applications side. I added few more features like handling GET request with query params and POST request etc,. Code is pretty well documented and followed the similar design. Will try to post the link of GitHub repo once push it there.
The points in this video are good. I will try to add on it a bit. Tests add more code. The hope is that you are "not affecting the design negatively", "writing a bug free test", "writing according to a valid spec", "increasing maintainability", "increasing faith in the code", "reducing points of regression" The reality however is this: "Designs can be affected negatively, when you are placing Testability above other things such as encapsulation and black-box" "Test code can have bugs too", "Tests could be invalid", "tests can increase maintenance", "tests that fail incorrectly or pass incorrectly can reduce faith", "you can break tests with refactoring and not reap the reward of regression tests". In order for tests to be a positive outcome, the spec should be correct, the test written perfectly, the code written perfectly. However, you can do that without many other great coding principles. You could have low cohesion and the tests pass. You could have high cyclomatic complexity and tests still pass. By this, you should understand that the tests are only as good as all the other factors, like good code writing, feedback, spec, requirements gathering etc etc. Add other negatives and test code may never equal a net positive. - increased learning curves - additional dependencies - cognitive load of code+tests+dependencyinjection - additional change chain reaction - additional dependencies (test harnesses) to manage On the other side of the coin. What if you increased your code reading skills? used design by contract, added fault tolerance to your code, added atomicity to your code, reduced the call stack, have aspect oriented dynamic checks, state validation, isolation etc?
This was a very good presentation.
How can I employ python pillow library in autolisp programming for kml file extraction from Google Earth?
Very nice tutorial. I guess my question is (5:16) - if eventually we are gonna link the posterior probability with p-value, why do we want to conduct Bayesian A/B test at the first place?