BC MACHINE LEARNING

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John Burn-Murdoch
@jburnmurdoch 7 years, 9 months ago 2004 views

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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?

More from Machine learning

Pratham Prasoon
@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)
- List slicing
- String formatting
- Dictionaries & Tuples
- Managing environments
- Dunder methods like __init__

(5 / 19)

IM POCUS Focus
@IMPOCUSFocus

A tweetorial on optimal machine settings for B line image acquisition, with a focus on: focal position at pleura, harmonics off, and increased far gain.

Inspired/based on the recent article - https://t.co/vv2xBSZm6N
@cameron_baston @IsaacMatthiasM1

#POCUS #IMPOCUS

1/18

Let's start with some polls
We will stick to low frequency probes here (i.e. curvilinear and phased array - linear has its own purpose for detailed pleural assessment)

Which preset do you prefer for B line assessment?

2/

What depth do you typically set?

3/

After selecting preset and selecting depth, do you ever adjust any other machine settings to optimize B line assessment?

4/

Importantly, there is no right answer to the above questions!

We won't discuss much about preset or probe type. But we will discuss machine settings to optimize B line quality, based on
1) Theory based on physical basis of B lines
2) In vitro study
3) Study in patients

5/

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Pratham Prasoon
@PrasoonPratham

Machine Learning for the Web developer in 2021.

The beginner's guide.

🧵👇

I started machine learning as a web developer, if I can do it then anyone can.

This carefully curated thread will give you key insights into my journey and how you can make this transition, seamlessly.

(2 / 14)

"Machine learning is not what you think it is"

One of the main reasons why people find it difficult to get started with machine learning is because of the lack of information, and rightfully so.

(3 / 14)

Machine learning as a concept has existed since the 1950s, but has only become popular in recent years because of the exponential rise of advancements in computer hardware.

(4 / 14)

In short, it because of the sudden rise of this technology ,which was previously unknown to the general public, that there is a lot of misinformation around it.

(5 / 14)

Pratham Prasoon
@PrasoonPratham

A simple guide on how to train your first neural network in under 10 minutes to kick-start your machine learning journey.

(+ no setup required and free extra resources)
🧵👇

For this exercise, you'll need :
- A computer/phone
- An internet connection
- Very basic Python knowledge
- Willingness to learn

What you do not need:
- Complex math
- An expensive computer
- A PhD

Math will come later on in your machine learning journey :)

(2 / 15)

Here's our problem,we are given data in which we are given the number of flats in a house and its corresponding price. Like a house with one flat is worth 10000, and 20000 for a house with 2 flats.

(3 / 15)

We can clearly tell that the price of the house increases by 10000 per extra flat however our computer does not know this and we won't it tell it about this, it'll have to figure things out on its own.

(4 / 15)

Here's the code👇

(5 / 15)

$Unknown \U0001f464$

Unknown 👤
@console_404

10 PYTHON 🐍 libraries for machine learning.

Retweets are appreciated.
[ Thread ]

1. NumPy (Numerical Python)

- The most powerful feature of NumPy is the n-dimensional array.

- It contains basic linear algebra functions, Fourier transforms, and tools for integration with other low-level languages.

Ref: https://t.co/XY13ILXwSN

2. SciPy (Scientific Python)

- SciPy is built on NumPy.

- It is one of the most useful libraries for a variety of high-level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization, and Sparse matrices.

Ref: https://t.co/ALTFqM2VUo

3. Matplotlib

- Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

- You can also use Latex commands to add math to your plot.

- Matplotlib makes hard things possible.

Ref: https://t.co/zodOo2WzGx

4. Pandas

- Pandas is for structured data operations and manipulations.

- It is extensively used for data munging and preparation.

- Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage.

Ref: https://t.co/IFzikVHht4

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Ankur Patel
@AnkurPatel59

FEW USEFUL CHARTINK SCANNERS :

This Scanner will be More than Enough for any Swing / Positional Trader to find Good Stocks.

Few Important Chart ink Scanners which can help you to find Setups and boost up your Trading Account : 🧵

Do Retweet If you If it Useful

1. Momentum Stocks

This Scanner can be helpful for Short term Swing Trades . you can easily find Stocks which are in Momentum.

Stocks which are up by 20 % in 5 Days
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Stocks which are up by 30 % in 30 Days

2. Volume Spike

We always Look for Price and Volume Confirmation for Breakout Trading. This scanner will help you to find the Stocks Which has given a move with Good Volume . You can keep this Stock in Radar and catch the Next move in them :

3 . Reversal Scan

Stocks which are Down by 20 % + in 5 days

When Market Corrects for sometime , You can find Stocks where selling is Exhausted and they can make a Good Reversal move .

4. Reversal Scan - Stock Down continues 5 days .

If you Know the Concept of 3-5 Days move . you might catch few stocks from this scanner. Always try and Pick stocks which are in strong momentum , you have more Chances of Getting Right in Reversal Trade.

Nikita Poojary
@niki_poojary

Catch me if you can @Mitesh_Engr

Time for a🧵
Mitesh Sir's EXPIRY Option Selling 101:
• What to look for?
• Strike Selection & Ratios
• SL mgmt
• Avoiding freezes
• Monthy Expiry
• Event days
• How he would have traded last expiry?

In collaboration with @AdityaTodmal

Edward de Bono says intelligence is like a🚗 Automobile

“A powerful car may be driven badly. A less powerful car may be driven well. The skill of the car driver determines how the power of the car is used.”

What @Mitesh_Engr Sir looks at for Expiry Trading:

• Technicals of BNF
• Individual Heavyweight stocks of BNF
• Support/Resistance of BNF on weekly TF
• Also has Superb SL management
• When it comes to adjustments, he is always on toes

How to Select Strikes to Sell?

• You can't sell based on premium
• Follow only trend
• Price action is best to sell on expiry day
• Never wait for the options to go to zero

Using Ratios to Sell:

• If BNF is in a range then strangles
• If breakout/ breakdown from a range then directional
• Selling 70/30 or 80/20 ratios
• 2 examples on how he used ratios

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John Myles White
@johnmyleswhite

This post is pretty bizarre, but it manages to hit on so many false beliefs that I've seen hurt junior data scientists that it deserves some explicit

(1) The notion that R is well-suited to "building web applications" seems totally out of left field. I don't feel like most R loyalists think this is a good idea, but it's worth calling out that no normal company will be glad you wrote your entire web app in R.

(2) It is true that Python had some issues historically with the 2-to-3 transition, but it's not such a big deal these days. On the flip side, I have found interesting R code that doesn't run in modern R interpreters because of changes in core operations (e.g. assignment syntax).

(3) "Most of the time we only need a latest, working interpreter with the latest packages to run the code" -- this is where things get real and reveal some things that hurt data scientists. If this sentence is true, it's likely because you don't share code with coworkers.

(3) Really is a broader issue in data science: people only think of what they need to do their work if no one else existed and code was never maintained. Junior data scientists almost always operate on projects they start from scratch and don't have to maintain for long.

Filippo Valsorda
@FiloSottile

“We don’t negotiate salaries” is a negotiation tactic.

Always. No, your company is not an exception.

A tactic I don’t appreciate at all because of how unfairly it penalizes low-leverage, junior employees, and those loyal enough not to question it, but that’s negotiation for you after all. Weaponized information asymmetry.

Listen to Aditya

"we don't negotiate salaries" really means "we'd prefer to negotiate massive signing bonuses and equity grants, but we'll negotiate salary if you REALLY insist" https://t.co/80k7nWAMoK
— Aditya Mukerjee, the Otterrific \U0001f3f3\ufe0f\u200d\U0001f308 (@chimeracoder) December 4, 2018

And by the way, you should never be worried that an offer would be withdrawn if you politely negotiate.

I have seen this happen *extremely* rarely, mostly to women, and anyway is a giant red flag. It suggests you probably didn’t want to work there.

You wish there was no negotiating so it would all be more fair? I feel you, but it’s not happening.

Instead, negotiate hard, use your privilege, and then go and share numbers with your underrepresented and underpaid colleagues. […]

Emma Hilton
@FondOfBeetles

This is Kuinini ‘Nini’ Manumua, the woman who was ultimately displaced by inclusion of Laurel Hubbard.

She’s 21, and it would have been her first Olympics.