jF3mO5aXiu

It's also a wonderful example of leveraging and customizing the fastai framework in a deep & thoughtful way.

Here's the full set of blog posts diving in to this project:

https://t.co/jF3mO5aXiu

https://t.co/DrCJxxJRAy

https://t.co/GgatWxa9nM

https://t.co/U9Yp5IZpxt

More from Data science

Goku Mohandas
@GokuMohandas

🔥 Putting ML in Production! We're going to publicly develop @madewithml's first ML service. Here is the broad curriculum:

- 📦 Product
- 🔢 Data
- 🤖 Modeling
- 📝 Scripting
- 🛠 API
- 🚀 Production

More details (lessons, task, etc.) here: https://t.co/xmMm9XGK9j

Thread 👇

Questions that this thread will answer:

- What is it?
- Who is this course for?
- What is the format?
- What makes this course unique?
- Why constrain to open source tools?
- What are my qualifications?
- Why is this free?
- What are the

What is it?

Putting ML in Production: a guide and code-driven case study on MLOps. We will be developing and deploying Made With ML's first ML service, from Product → ML → Production, with open source tools.

This ML service will act as a foundation for all future ML features and subsequent iterations. The first feature is tagifai - multilabel classification of tags for a project. We'll discuss the need and utility of this feature in the first lesson.

Who is this course for?

- ML developers looking to become end-to-end ML developers.
- Software engineers looking to learn how to responsibly deploy and monitor ML systems.
- Product managers who want to have a comprehensive understanding of the different stages of ML dev.

Data Professor
@thedataprof

Cheat sheet that summarizes #DataScience in 10 pages
(Links in the comments below 👇)

2/ Link to the cheatsheet by Maverick

Sophie Hill
@sophie_e_hill

This is a more wonky thread about how I made this visualization in #Rstats using the awesome visNetwork

First step is to create the underlying network data. We need one file of "nodes" - i.e. the people and organizations. And one file of "edges" - i.e. the connections between them.

I created these by hand, based on excellent investigate journalism:

Now we can pull these together to create a network visualization!

You'll notice that I included a column for "type" in the nodes file. This allows me to use different icons for people vs firms vs political organizations.

All the icons are taken from @fontawesome. I *think* the visNetwork 📦 currently only works with fontawesome version 4.7, which is a bit limited – e.g. I decided to use a book icon to represent the fringe Evangelical Christian sect "Exclusive Brethren"! 😂

I very much enjoyed getting to use the "incognito" icon to represent all the unknown donors that have funded Tory MP Owen Paterson's overseas jaunts!

Emil Wallner
@EmilWallner

Tips for AI writers:

1. Spend 30% of your effort on skimming all student ML papers (e.g. Stanford NLP CS224n) the past 3 years and prototype your favorites

The idea is everything. Pick an area you are interested in and ideally something that has a visual aspect to it

Most of my 'on the top of my mind' ideas were bad in retrospect. Skimming 100s of student papers will give you an overview of what's interesting.

Student papers are overlooked, easy to understand, and have good compute constraints.

2. Spend 30% on your effort on coding

Create an edge to the project. Apply it to something new and use FastAI or Keras to improve the accuracy with 5-30%.

3. Spend 30% writing an in-depth article

Have a north star article in terms of structure and quality. Find something that stretches you to your utmost capability. I used @copingbear’s Style transfer article:

4. Spend 10% marketing your project

Invest a week in studying the strategies to rank on sites like HN and Reddit, then use them. If you have an interesting result and a great article, you've done the hard work.

Simon DeDeo
@SimonDeDeo

On Bayesianism, the Many Worlds Interpretation, and personal identity.

Some thoughts worked out in a letter to a friend, which is the kind of thing you do when off Twitter for a glorious week. (🧵)

“Chance is ignorance”—the Bayesian story; all probabilities represent states of mind, not states of the world. One *could* put (some) chances “in the world”, but let’s take Occam’s Razor seriously...

That the probability of a fair coin coming up heads is 50% simply means that marginalizing (tracing, as the physicists say) over the hidden facts leaves you, nearly, maximally ignorant of the outcome.

Quantum uncertainty (access below!) poses an apparent challenge to this story. There seems to be nothing to be ignorant about when it comes to (say) electron spin—there is nothing “inside” the

The electron is a simple object, in other words. So where does the uncertainty come from? One could follow David Wallace’s wonderful interpretation in terms of chaotic dynamics and decoherence, but let’s see if we can take another route...

2/ Starting from the first replicating molecule which co-operated with an outer layer to form first proto-cell, evolutionary and cultural history is full of examples where two entities come together to survive and progress a lot more than they would have done individually.

3/ This co-operative entity fares much better than two individual entities because of specialization. If two entities are in the same boat - that they win together or lose together - then trust is implicit.

4/ In a non-zero sum game, trust causes entities to focus on what they do best.

For example, eukaryotic cells - the ones animals and plants have - emerged when two proto-cells merged, and one took the role of energy generator (mitochondria), the other specialized in protection.

5/ Some scientists believe that even nucleus in a cell is a result of an early cell merging with another cell.

So M&A is not a recent phenomena, entities have been merging because of common interests ever since life started.

Jim O'Shaughnessy
@jposhaughnessy

I’m torn on how to approach the idea of luck. I’m the first to admit that I am one of the luckiest people on the planet. To be born into a prosperous American family in 1960 with smart parents is to start life on third base. The odds against my very existence are astronomical.

Ironies of Luck https://t.co/5BPWGbAxFi

"Luck is the flip side of risk. They are mirrored cousins, driven by the same thing: You are one person in a 7 billion player game, and the accidental impact of other people\u2019s actions can be more consequential than your own."
— Morgan Housel (@morganhousel) March 14, 2018

I’ve always felt that the luckiest people I know had a talent for recognizing circumstances, not of their own making, that were conducive to a favorable outcome and their ability to quickly take advantage of them.

In other words, dumb luck was just that, it required no awareness on the person’s part, whereas “smart” luck involved awareness followed by action before the circumstances changed.

So, was I “lucky” to be born when I was—nothing I had any control over—and that I came of age just as huge databases and computers were advancing to the point where I could use those tools to write “What Works on Wall Street?” Absolutely.

Was I lucky to start my stock market investments near the peak of interest rates which allowed me to spend the majority of my adult life in a falling rate environment? Yup.