Ben Hamner
@benhamner
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working on something new. formerly @kaggle cto
SF
Joined March 2009
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Wordle xxx 1/6
🟩🟩🟩🟩🟩
Get Wordle right on your first guess using the daily ⬛🟨🟩 tweet distribution
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Let’s make a deal: America will adopt the metric system, Europe will adopt 10,000,000.00-style number formatting
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Programming: 10% writing code. 90% figuring out why it doesn’t work
Analyzing data and ML: 1% writing code. 9% figuring out why code doesn’t work. 90% figuring out what’s wrong with the data
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@brianwilt
Both America and Europe are wrong here. ISO-8601 dates (YYYY-MM-DD) is a hill I will die on. Unambiguous and sortable as strings!
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Between ChatGPT and GitHub Copilot I think I spoke more to AIs this week than humans
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Easy parts of applying machine learning:
.fit()
.predict()
Hard parts:
.clean()
.transform()
.get_data()
.frame_problem()
.debug()
.handle_nonstationarities()
.handle_missing_inputs()
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Replace "AI" with "matrix multiplication & gradient descent" in the calls for "government regulation of AI" to see just how absurd they are
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Excited to launch Kaggle Learn - interactive tutorials on machine learning, deep learning, R, and data visualization
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The blockchain movement is 100x worse than the NoSQL movement. Every time I see a new blockchain idea I ask “would a relational DB be unambiguously better in every regard here?” (generating page views expected). 99% of the time the answer’s yes
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VS Code data structure visualization extension. This is neat
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pandas pro tip: use .format(thousands=",") to make larger numbers legible (I'm confused why this isn't the default display)
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How did a tiny research team at OpenAI outperform thousands of scientists at Microsoft Research?
Turns out they used Google Meet instead of Microsoft Teams
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Deep learning and AI get all the buzz/press. The untold story is all the hard valuable work to create high quality datasets that enable them
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It’s embarrassing and infuriating that some
#NIPS2017
authors couldn’t get visas to present their work. USA should be leading through enlightened example, not disgusting racism. We lose when we can’t attract the top AI minds, whatever they look like and wherever they’re born
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When you write code, keep in mind that you're collaborating with your future self
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Whoa! Pandas has a nifty function read_html for pulling a webpage, returning a list of dataframes representing the tables on it
I wanted sunrise/sunset for SF, and thought I was going to have to get my hands dirty parsing
Nope! It's a pandas one-liner
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Want to learn how to use Keras and Tensorflow to apply deep learning to computer vision problems? Great set of intro videos + exercises by
@dan_s_becker
on Kaggle Learn
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Federated learning: train machine learning models while preserving user privacy, by keeping user data on device (e.g. mobile phone) and only sending encrypted gradient updates (that can only be decrypted in aggregate) back to the server
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A 3% decrease in California almond production would save as much water as completely shutting off water usage in SF (all homes, businesses)
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Feature stores for ML: nice collection of tech talks from companies that have rolled their own feature stores for productionizing the data that feeds ML applications
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93% of public, upvoted Python kernels on Kaggle use pandas
@wesmckinn
. The only two other libraries directly imported >50% of the time are numpy (89%) and matplotlib (59%). Impossible to understate the impact pandas has had on the PyData ecosystem
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Wow. This may be the most effective data visualizations I’ve ever seen. Brilliant use of a green screen. Worth watching all the way through
Storm surge will be a huge factor for Hurricane
#Florence
Check out what it might look like with
@TWCErikaNavarro
:
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It's crazy how much our universities focus the next generation on test results, course completions, and degrees. I wish they empowered students to create and build. The transcript they should be aiming for is "here's the ten best things we created during our time here"
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Pandas is a swiss army knife for working with data!
This
@kaggle
notebook highlights 100 tricks
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Statistics: you can't add probabilities like that!
Machine learning: ¯\_(ツ)_/¯ it improves my model performance
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Data visualization in Python - nice set of interactive notebook tutorials by
@ResidentMario
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Three most used datasets in
#NIPS2017
:
1. MNIST (110 papers)
2. CIFAR (79 papers)
3. ImageNet (60 papers)
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As data scientists, when an analytics result doesn't match our expectations, we scrutinize everything to explain it (data issues, code issues, etc.). This scrutiny often finds bugs that overturn the result.
I worry that we only apply this scrutiny and rigor to unexpected results
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Deep convolutional neural network trained and evaluated on 200,000 breast cancer exams achieves an AUC of 0.895, equivalent to expert radiologists. A hybrid model combining the radiologist and machine readings achieves the best results
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Looks like NIPS 2018 may have sold out in under 15 minutes. For those debating ML hype, getting a ticket to a ML conference is now more challenging than a Taylor Swift conference or a Hamilton showing
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Kaggle Kernels now supports GPU’s! You can attach one to your kernel through the settings tab. Here’s an example of training a model on a GPU
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I just trained a 1 trillion parameter neural net!
All parameters just happen to be 0
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I have one problem: I need to install a Python package
Great, now I have 99 problems
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We now have over 10,000 public datasets shared on Kaggle! This is a key milestone in our mission to help the world learn from data
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tqdm in Python notebooks insanely easy to use:
"for i in my_list:" becomes "for i in tqdm(my_list):"
and you get a beautiful progress bar and ETA left for any long-running loop
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Most online courses are incentivized to get you to waste time on more online courses. We launched Kaggle Learn as a series of small, bite-sized tutorials because the best way to learn AI is developing your own projects as quickly as possible
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Tech journalists have successfully predicted 1,000 of the past 1 bubbles
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We just launched a
@kaggle
challenge focused on open
#COVID19
research questions, including data set of 29,000 relevant papers to help:
Thanks White House
@WHOSTP
@allen_ai
@NIH
@Microsoft
@Georgetown
@ChanZuckerberg
for rapid collaboration on data
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Fears of machine intelligence putting data scientists out of work is like being scared of compilers eliminating programming jobs in the 70s
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To all gamers who got told you weren't doing good for society:
A massive thanks for funding GPU R&D, which enabled this wave of AI advances
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What machine learning commentators talk about: deep neural net flavor du jour, AI risk
What machine learning practitioners talk about: messy data, data labeling, tuning learning rates, collecting more data, feature representation, cost functions, latency, productionization, ...
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Everyone gets jazzed about ML algorithms
High quality, context-appropriate data is the crucial enabler for every application I've touched
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Kaggle now has an API for downloading data and submitting to competitions!
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Most AI breakthroughs constrained by high quality datasets, not algorithms
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Saw a comment that there's close to a 1% chance of dying from a car accident. Was shocked it's this high. Back-of-the-enveloped the math, and it pans out
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Want to convert a daily time series to a weekly moving average?
df["col"].rolling(7).mean()
pandas is delightful
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I had a cron job running for several weeks to notify me when a swim lesson spot opened up for my 10 month old
Is this peak SF tech parent?
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GitHub CoPilot's a super cool technology, but it's as close to automating your code writing as Gmail Smart Compose is to automating your email writing
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One big takeaway from Kaggle's second kernels competition: limiting compute is an incredibly effective regularizer on model complexity
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“The missing semester of your CS education” - looking at the syllabus, this is probably the most important set of skills to master for programming in practice. The shell, git, data wrangling, debugging, etc.
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I bet the average length of hair in the US right now’s the longest it’s been in a century
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The rules of machine learning: best practices for ML engineering by Martin Zinkevich
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Hiring several backend/data/ML engineers for our new(ish) company, focused on building high-quality structured data from raw, noisy inputs. Have funding, revenue, users
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Using Kaggle to start and guide your ML/data science journey
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One big ML pain point has been putting models into production. It's now possible to incorporate Jupyter notebooks directly into production workflows, making this one step easier!
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Headline: "Killer AI will take over the world"
Reality: "High quality datasets, addition, and multiplication empower the global economy"
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Want to easily download the data on Kaggle? Use our API and CLI
> pip install kaggle
> kaggle datasets download -d rtatman/lego-database
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One of our big focuses at
@kaggle
is improving the quality of the public data ecosystem. As part of this, we launched dataset usability ratings on 17000+ public datasets to promote better practices around documentation and tutorials
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"Scheduling notebooks at Netflix". cron on notebooks is a powerful idea - we've been thinking about how we want to incorporate this into Kaggle
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Our newest
@kaggle
competition is OCR for chemical compounds. Can you apply ML to translate from an image of the chemical structure to the text string that represents it? 4 million chemical structure images to help solve this problem!
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She had the right response. Gotta have standards, and this guy just wasn't up to ISO 8601
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Publish your dataset on Kaggle, and our new Kaggle Kerneler bot will write an automatic exploratory analysis on it for you in Python, showing you how to load and get started on the data
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One new Kaggler learned about machine learning during her maternity leave and finished in the top 2% of a challenge on identifying cell nuclei
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Have you wanted to start learning Python for data and analytics but never taken the leap?
Sign up for Kaggle’s “Learn Python” track, where you’ll learn to apply Python to a fun 20-minute puzzle every day from June 11-17
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We now have over 20,000 datasets published on Kaggle! 📈🎉🎊🙌
Thanks to our designers+engineers hard work to build a platform for this, and to all of you, for making data you can open+accessible, and sharing your reproducible notebooks on these datasets
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Browser tabs on my computer multiply like rabbits. And then every once and a while there is a mass extinction event that forces the system to restart from scratch
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"Sir, you're under arrest for attempted international terrorism. Setting learning rate α=100000 is above government-approved safe values"
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We’re starting to formally invite automated machine learning tools to submit benchmark solutions to
@kaggle
competitions
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37% of Silicon Valley was born outside the US. This number would even higher if there weren’t structural barriers in place to recruiting world-class talent, no matter where they happened to be born
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Getting started with machine learning and want to explore different libraries and ideas? Here's some of our favorite ML-friendly public datasets on Kaggle that are (mostly) clean and easy to work with
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Keras is the primary ML framework used by competition winners on Kaggle since 2016. Congrats
@fchollet
for creating an API that's incredibly intuitive and easy to get started with, while being flexible and powerful enough for state-of-the-art performance
What machine learning tools do Kaggle champions use? We ran a survey among teams that ranked in the *top 5* of a competition since 2016.
The first question asked about the *primary* framework they used.
Very happy to see confirmation that winning teams prefer Keras :)
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It's funny how many people are worried about AI automating almost every job but their own.
From the outside, it's easy to overlook the complexities inherent in other's jobs, and how far we are from automating almost all of them in practice.
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White themes too bright for coding your Jupyter notebooks?
@noderaider
just launched a dark theme for editing
@Kaggle
Kernels.
Welcome to the dark side, Kernels
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This 👇. Sadly, trying to reproduce machine learning results from a PDF is kinda like trying to reproduce an extravagant dish from its Instagram photo. Sharing code and data, and starting from that, are critical!
This has come up again, so I’m going to repeat it:
If you’re learning ML and want to “reimplement a paper”, you should work from the *github code*, NOT the pdf.
The algorithm that the authors actually ran is often subtly (& unintentionally) different from what the paper says.
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Popular datasets referenced over time in NIPS papers. Surprisingly, MNIST reigns king
#nips2016
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Data science glossary on
@kaggle
- a great curated list of kernels providing forkable and reproducible tutorials on machine learning algorithms
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Many comments online are toxic and harassing. We want to provide the tools to detect and fix this using machine learning
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Super confused why we still use resumes. Get 100x the signal from domain profiles (GitHub, StackOverflow, Kaggle, etc.) & real work samples
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Want to get started with game AI programming? Try the latest
@kaggle
simulation competition: Lux AI, a 1v1 resource-gathering game to produce enough light for your city to survive the night
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You can now query all historic Bitcoin blockchain transactions through Kaggle Kernels. Here's a visualization of the network that led to the 10k Bitcoin pizza transaction early on
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One-liner to make colleague lose all credibility:
echo -e "library(ggplot2)\nlibrary(ggthemes)\ntheme_set(theme_excel())" >> ~/.Rprofile
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Deep learning papers reading roadmap
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On August 2nd,
@kaggle
is kicking off "30 days of ML" for those new to ML to learn the basics in an hour a day of structured, hands-on challenges. No prior coding experience necessary!
Sign up here:
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Let's agree that we won't call sophisticated (or unsophisticated) forms of regression "artificial intelligence" when speaking to journalists
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Why did the naive Bayesian feel patriotic when they heard fireworks?
They assumed independence! (HT
@wzchen
)
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One company's data scientist
is another's quant
& another's analyst
& another's developer
& another's ML engineer
& another's DB admin
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