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...

More from Machine learning

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.
MACHINE LEARNING
Santiago
Santiago
@svpino
11 key concepts of Machine Learning.

— Supervised Learning Edition —

🧵👇

😜

Before starting, remember that, if you follow me, one of your enemies will be immediately destroyed (and you'll get to read more of these threads, of course.)

And if you don't follow me, well, you just hurt my feelings.

😜

1. Labels

(Also referred to as "y")

The label is the piece of information that we are predicting.

For example:

- the animal that's shown in a picture
- the price of a house
- whether a message is spam or not

👇

2. Features

(Also referred to as "x")

These are the input variables to our problem. We use these features to predict the "label."

For example:

- pixels of a picture
- number of bedrooms of a house
- square footage of a house

👇

3. Samples

(This is also known as "examples.")

A sample is a particular instance of data (features or "x.") It could be "labeled" or "unlabeled."

👇
MACHINE LEARNING
Advertisement
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Here's what you'll hear when you first get into machine learning.

- Neural networks
- Loss
- Weights
- Biases
- Epochs
- Neurons
- Optimizers
...

It can get very confusing really fast!

Here are some of the terms you should know about.
(I wish I had this before)

🧵👇


These terms won't mean anything unless you know what Machine learning is all about.

> Machine learning is the process of making a program which allows a computer to learn from data.

The data could be anything, images, audio or even text.

In machine learning we use something called a neural network, this is essentially an imitation of the human brain.

> Neural Networks are a digital imitation of the neurons you see in the human brain.


In these neural networks, data flows through them and each neuron (the circle) has a numerical value which will change.

> The value of a neuron gets changes to something which is close to what we want each time the data passes through the neural network.


Think of the neurons as dials on a lock, you have to tune every dial to open the lock.

It is almost impossible for a human to tune thousands of dials like these, but a computer certainly can.
MACHINE LEARNING
Pratham Prasoon
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)
MACHINE LEARNING
Caio Rolla
Caio Rolla
@caio_rolla
Starting a new project using #Angular? Here is a list of all the stuff i use to launch my projects the fastest i can.

A THREAD 👇

Have you heard about Monorepo? I created one with all my Angular (and Nest) projects using https://t.co/aY5llDtXg8.

I can share A LOT of code with it. Ex: Everytime i start a new project, i just need to import an Auth lib, that i created, and all Auth related stuff is set up.

Everyone in the Angular community knows about https://t.co/kDnunQZnxE. It's not the most beautiful component library out there, but it's good and easy to work with.

There's a bunch of state management solutions for Angular, but https://t.co/RJwpn74Qev is by far my favorite.

There's a lot of boilerplate, but you can solve this with the built-in schematics and/or with your own schematics

Are you not using custom schematics yet? Take a look at this:

https://t.co/iLrIaHVafm
https://t.co/3382Tn2k7C

You can automate all the boilerplate with hundreds of files associates with creating a new feature.
MACHINE LEARNING

You May Also Like

The Astro Sapien\u2728
The Astro Sapien✨
@TheAstroSapienn
Here is my Thread on 'Ketu (South Node) Through Nakshatras' The 27 Constellations 🙏❣.

Do check your “Past life Mastery” which KETU Represents🤞🏻.


1. Ketu in Ashwini Nakshatra:

Here native is Great at Financial Planning, Face reading, understanding other’s mind and intention, Driving Fast they don’t like but they are wayyy smooth over driving Fast. This is also great position which can make native Great Surgeon,

Neurologist, Psychologist.
These natives are Selfless and Sacrifice self for others. They gets Fame and Success but they just don’t care.

2. Ketu in Bharani Nakshatra:

They are good at Flirting, Attracting opposite sex, studing ancient subjects. They are also Great at Creativity and Artistic activities. The are lazy but they are really open minded who handles situations really well.

In Past life they have handles many things which is now helping them to make things easier in this life. They are great Leaders too.

They are really good at Hard work and Smart work both. And yes just as Ashwini they don’t much care about Fame and Success!
ALL
Jaya_Upadhyaya
Jaya_Upadhyaya
@Jayalko1
SRI BANKEY BIHARI JI, VRINDAVAN (UP)

Very famous Krishna Mandir where Bankey Bihari Ji is worshipped and looked after as a child.
Bankey means “Bent at three places”.
Thakur Ji was worshipped in Nidhivan till 1863. This mandir was constructed in 1864 by Goswamis.
@LostTemple7


Legend says that Swami Haridas used to worship Krishna in Nidhivan.
Once, his curious disciples entered the forest but they were almost blinded by bright, intense light. Upon knowing this, Swamiji himself went there and after his requests, Krishna appeared with his consort.


Krishna, then, left back this black charming murti,which we see now, before disappearing.
A curtain is drawn before the murti after every 15-30 second. It is said that if one stares long enough into the eyes of Shri Banke Bihari, the person would lose his self consciousness.


Bankey Bihari Ji sports a flute only once a year on the eve of Sharad Purnima.
Darshan of His lotus feet can be done only on Akshay Tritiya every year.
There are no loud bells here and
no early morning Darshan can be done so that child Krishna’s sleep is not disturbed.
ALL
Advertisement
Vikrant
Vikrant
@Trading0secrets
Full technical analysis courses in one thread 🧵

For free which is Worth ₹80k 👇

⏭️How elliott wave work with examples

⏭️ Advance price action analysis

⏭️ Richard wyckoff theory

⏭️dow theory (my first video)

If you wanna learn how market behave in long term & short term

You must watch full video 🎥
SCREENERS , ALL , TECHNICALANALYSIS
Erik Torenberg
Erik Torenberg
@eriktorenberg
1/“What would need to be true for you to….X”

Why is this the most powerful question you can ask when attempting to reach an agreement with another human being or organization?

A thread, co-written by @deanmbrody:


2/ First, “X” could be lots of things. Examples: What would need to be true for you to

- “Feel it's in our best interest for me to be CMO"
- “Feel that we’re in a good place as a company”
- “Feel that we’re on the same page”
- “Feel that we both got what we wanted from this deal

3/ Normally, we aren’t that direct. Example from startup/VC land:

Founders leave VC meetings thinking that every VC will invest, but they rarely do.

Worse over, the founders don’t know what they need to do in order to be fundable.

4/ So why should you ask the magic Q?

To get clarity.

You want to know where you stand, and what it takes to get what you want in a way that also gets them what they want.

It also holds them (mentally) accountable once the thing they need becomes true.

5/ Staying in the context of soliciting investors, the question is “what would need to be true for you to want to invest (or partner with us on this journey, etc)?”

Multiple responses to this question are likely to deliver a positive result.
LIFE
Michael Dearing
Michael Dearing
@mcgd
An irony of facebook's situation.

Sheryl got her MBA at Harvard. One of the most famous cases (Extra Strength Tylenol) in one of the most famous classes (Business History) she took: in 1982, someone put cyanide in Extra Strength Tylenol capsules and killed 7 people in Chicago.

What do you do when someone turns your product into a weapon? When they use the system you built to harm? James Burke, CEO of J&J, was shockingly open with the public, he pulled the product and made significant packaging changes to make product safer (but not tamper-proof).

He over-shared every step along the way re investigation, redesign, stood up as both CEO and human. The reintroduction of new Extra Strength Tylenol succeeded. Burke saved the brand.

But four years later it happened again. A killer put cyanide in the capsules, this time a woman in Yonkers died. Same CEO, Burke, pulled the product again, completely changed the form factor from capsule to caplet and relaunched *again*. It worked *again*. How'd they do that?

Burke (CEO) tapped J&Js goodwill bank account w/ the public. Two big withdrawals from that bank account in four years + 8 dead bodies! But his honesty, openness, humanity (choked up about the deaths more than once), humility kept the goodwill bank balance positive the whole time.
TECH