BC MACHINE LEARNING
Saved by @SteveeRogerr

1/🧵 Good #DataScience advice that breaks pretty much every rule you learned in class... a thread. (+full blog post linked)

English version: https://t.co/dG4l6vPFBT
Spanish version: https://t.co/gFAjPQ5clS

#AI #MachineLearning #Statistics #RStats

 2/🧵 Allow your approach to be sloppy at first and burn some of your initial time, energy, and data on informing a good direction later. That's right, you're supposed to start sloppily ON PURPOSE.
3/🧵 Have a phase where the only result you’re after is *an idea of how to design your ultimate approach better.*
4/🧵 In other words, start with a pilot phase where the objective isn't finding answers, it's finding a good approach to finding answers.
5/🧵 That means you're encouraged (ENCOURAGED!) to start with everything your stats classes told you not to do:
6/🧵 Low-quality data: use small sample sizes, synthetic data, and non-randomly sampled data to gain insights about the data collection process itself.
7/🧵 Rough-and-dirty models: seek an understanding of what the payoff from minimum effort looks like. Start with bad algorithms which you know are only going to give you a benchmark, not your best solution.
8/🧵 Multiple comparisons: instead of picking a single hypothesis test, feel free to throw the kitchen sink at your data to discover signals worth basing your final approach on. Add deadlines and MVP milestones to avoid the trap of infinite polishing, poking, and prodding.
9/🧵 If the statistician in you isn’t screaming yet, I admire your sangfroid. This advice breaks pretty much every rule you learned in class. So why am I endorsing these “bad behaviors”?
10/🧵 So why am I endorsing these “bad behaviors”? Because this is the pilot phase. I’m all about following the standard advice later, but this early phase has different rules.
11/🧵 The important thing is to avoid rookie mistakes by remembering these 2 crucial principles:
12/🧵 Principle 1: Don’t take any findings from the early phase too seriously.
13/🧵 Principle 2: Always collect a clean new dataset when you’re ready for the final version.

For more info: https://t.co/Ue332SMjy1
14/🧵 You’re using your initial iterative exploratory efforts to inform your eventual approach (which you’ll take just as seriously as the most studious statistician would). The trick is to use the best of exploratory nimbleness to inform what’s worth considering along the way.
15/🧵 If you’re used to the rigidity of traditional statistical inference, it’s time to rediscover the benefits of pilot studies in science and find ways to embed the equivalent into your data science projects.
16/🧵 The key thing to understand about this advice is that

- finding good questions
- finding good answers
- finding good approaches going from one to the other

are all different objectives that require different approaches. Sometimes there's homework to do before answers...

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Really enjoyed digging into recent innovations in the football analytics industry.

>10 hours of interviews for this w/ a dozen or so of top firms in the game. Really grateful to everyone who gave up time & insights, even those that didnt make final cut 🙇‍♂️ https://t.co/9YOSrl8TdN


For avoidance of doubt, leading tracking analytics firms are now well beyond voronoi diagrams, using more granular measures to assess control and value of space.

This @JaviOnData & @LukeBornn paper from 2018 referenced in the piece demonstrates one method https://t.co/Hx8XTUMpJ5


Bit of this that I nerded out on the most is "ghosting" — technique used by @counterattack9 & co @stats_insights, among others.

Deep learning models predict how specific players — operating w/in specific setups — will move & execute actions. A paper here: https://t.co/9qrKvJ70EN


So many use-cases:
1/ Quickly & automatically spot situations where opponent's defence is abnormally vulnerable. Drill those to death in training.
2/ Swap target player B in for current player A, and simulate. How does target player strengthen/weaken team? In specific situations?
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Pratham Prasoon
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@PrasoonPratham
Are you planning to learn Python for machine learning this year?

Here's everything you need to get started.
🧵👇

In this thread, we'll look at all the concepts in Python you need to know for machine learning along with free resources to help you out.

All of this is based on my experience of successfully teaching 300+ students how to code using Python.

(2 / 19

You can use many languages for machine learning, why Python?

Because of 2 reasons:
- Comparatively easier to learn than other languages
- Has the biggest and most mature community

This makes Python a no-brainer to learn for machine learning as a beginner.

(3 / 19)

These are the absolute basics which you must know about:

- Basic terminal commands
- Basic arithmetic (+,-,/,*)
- Accepting user input
- For & While loops
- Exception handling
- If-Else statements
- Functions, modules & Imports

(4 / 19)

Then comes the more tougher concepts which you must know about:

- Object oriented programming in Python:Classes, Objects, Methods
- PIP (Pypi)
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- Dictionaries & Tuples
- Managing environments
- Dunder methods like __init__

(5 / 19)
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Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Do you want to learn the maths for machine learning but don't know where to start?

This thread is for you.
🧵👇

The guide that you will see below is based on resources that I came across, and some of my experiences over the past 2 years or so.

I use these resources and they will (hopefully) help you in understanding the theoretical aspects of machine learning very well.

Before diving into maths, I suggest first having solid programming skills in Python.

Read this thread for more


These are topics of math you'll have to focus on for machine learning👇

- Trigonometry & Algebra

These are the main pre-requisites for other topics on this list.

(There are other pre-requites but these are the most common)

- Linear Algebra

To manipulate and represent data.

- Calculus

To train and optimize your machine learning model, this is very important.
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Anson Horton
Anson Horton
@AnsonHorton
1/ It's probably not the first thing you think of, but when we started .NET (COM+) in the late 90s, C# didn't exist yet. We were working on it at the same time as the CLR and the framework. So, you might wonder, what language was being used to generate IL and write the BCL?

2/ The answer is a language that we called SMC that Peter Kukol wrote the compiler for. Peter is a flat out amazing engineer and wrote the core parts of the compiler in just a few days. This unblocked the framework team, allowed vetting the runtime and interpreter, etc.

3/ SMC was a trimmed down C++ variant and the compiler was written in itself (i.e. SMC). It didn't support things like destructors, multiple inheritance, virtual base classes, etc. But, overall it enabled progress that would have otherwise been stalled.

4/ It did introduce a problem for the C# language and compiler team though. Every day more and more code was written in SMC, a language that we did not intend to ship externally. Designing a new language is hard (well, at least, a general purpose one that becomes popular).

5/ So there was a constant balancing act between laying enough of the foundation of the language and the C# compiler to replace SMC and making sure that the design lived up to the goals and ideals the team was striving for. If we waited too long to adopt C#,
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Jan Giacomelli
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@jangiacomelli
🤔 Python generators

What are they? How to use them?

#Python

🧵Let's find out 👇

1️⃣ Python generators are lazy iterators delivering the next value when their .next() is called.

They are created by using the yield keyword

next() can be called explicitly or implicitly inside for loop

They can be finite or infinite


2️⃣ yield - where a value is sent back to the caller, but the function doesn’t exit afterward as with the return statement

The state of function is remembered.

For example, the number is incremented and sent back from yield at the consecutive call next()


3️⃣ Generator stores only the current state of the function - it generates next element on next() call and forgets the previous one -> it saves memory

For example, we don't need to store 1mio elements in memory to do something with each element


4️⃣ When you call a generator function generator object is returned - it's not executed yet

It executes only when next() is called

For example, that's why Exception is raised only on the next() call
MACHINE LEARNING

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