BC DS
Saved by @AnmolNarula11

The two main machine learning techniques used in the industry today:

1. Gradient Boosted Trees
2. Deep Learning

Focus your time learning Scikit-Learn, XGBoost, and a Deep Learning library like Keras or PyTorch and you'll get the most for your time.

 If you need to deal with structured data: Scikit-Learn + XGBoost.

If you need to deal with unstructured data (perceptual tasks): Keras or PyTorch.
Of course, there are many more techniques that will be helpful, but if you focus on these you'll be maximizing your ability to deliver value in the short term.

More from Santiago

Santiago
Santiago
@svpino
12 machine learning YouTube videos.

On libraries, algorithms, tools, and theory.

↓

1. Jupyter Notebooks: https://t.co/HqE9yt8TkB

2. Pandas: https://t.co/aOLh0dcGF5

3. Matplotlib: https://t.co/tKADpmihkh

4. Seaborn: https://t.co/s8EUxh6x1f


5. Numpy: https://t.co/pJoc0Lfjwm

6. Decision Trees: https://t.co/tKtUpO1K3l

7. Neural Networks: https://t.co/bc7emyjc9q

8. Scikit-Learn: https://t.co/LrKG7cMxRq


9. TensorFlow: https://t.co/fhO6T9sblU

10. PyTorch: https://t.co/5w9mJxijdd

11. Essense of Linear Algebra: https://t.co/o3kOnxl90i

12. Essense of Calculus: https://t.co/rfo7v0cpR4
ALL
Santiago
Santiago
@svpino
An introduction to one of the the most basic structures used in machine learning: a tensor.

🧵👇

Tensors are the data structure used by machine learning systems, and getting to know them is an essential skill you should build early on.

A tensor is a container for numerical data. It is the way we store the information that we'll use within our system.

(2 / 16)

Three primary attributes define a tensor:

▫️ Its rank
▫️ Its shape
▫️ Its data type

(3 / 16)

The rank of a tensor refers to the tensor's number of axes.

Examples:

▫️ The rank of a matrix is 2 because it has two axes.
▫️ The rank of a vector is 1 because it has a single axis.

(4 / 16)

The shape of a tensor describes the number of dimensions along each axis.

Example:

▫️ A square matrix may have (3, 3) dimensions.
▫️ A tensor of rank 3 may have (2, 5, 7) dimensions.

(5 / 16)
MACHINE LEARNING , TENSOR BASICS
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Santiago
Santiago
@svpino
Machine learning education is broken.

If you are preparing for a research position, you are good. If you are looking to get out there and start solving problems, not even close.

Here are some thoughts so you can get ahead.

↓

Most classes, courses, and books cover the same road.

They start with a dataset. They finish with a working model. The focus is always on everything that happens in between.

Dataset → Model.

This is great, but not enough.

Real-life situations rarely start with a dataset, and they never end after you finish building your model.

Applying machine learning successfully is hard.

Here are a few examples that you should keep in mind.

First challenge: Properly framing up the problem.

If you don't understand the problem, you can't determine what data you need. If you don't understand the data, you can't build a good model.

I've never seen a company that had their data ready to go.

In fact, most of them don't even have data at all and need you to determine what exactly they should start collecting.

You usually have to go Problem → Potential Solution → Data.
ALL
Santiago
Santiago
@svpino
Some of the things I've learned in more than 20 years in the tech industry.

You need to hear these.

🧵👇

Listen to more people who don't look like you, don't speak your language, and don't come from the same place you do.

We aren't doing this near enough.

Small habits compound.

No small improvement is too small.

Just aim for something new every day, and you'll be surprised at the end.

Take responsibility.

It doesn't really matter how you feel, I'm sure you could have done better.

Always keep the big picture in mind and never lose the forest for the trees.

Be the person that pulls everyone out of the rabbit holes.
TECH
Santiago
Santiago
@svpino
Do you know what's better than a machine learning model?

Two models.

More than one model working together to solve a problem is called "an emsemble." A simple way to build this is having each model vote for an answer.

But there's a problem with this approach: ↓

I'm gonna focus here on image classification.

Let's assume you built two different models:

• Model 1: A ResNet model.
• Model 2: A one-shot model (Siamese network.)

They both solve the same problem, so you want to combine their results to pick the right answer.

The problem is that you have two models, so voting is not trivial.

What happens in this case?

• Model 1's answer: Class A
• Model 2's answer: Class B

Which one do you select?

Notice that this problem is not limited to an even number of models.

You could have 3 models, each giving you a different answer.

How do you decide which answer to choose?

There are multiple ways to approach this problem. I'll mention a few different ideas on this thread.

Important: Some of these ideas might not be feasible depending on your context. They have worked for me before on different situations, but every problem is different.
DS

More from Ds

10-K Diver
10-K Diver
@10kdiver
1/

Get a cup of coffee.

In this thread, I'll walk you through 2 probability concepts: Standard Deviation (SD) and Mean Absolute Deviation (MAD).

This will give you insight into Fat Tails -- which are super useful in investing and in many other fields.


2/

Recently, I watched 2 probability "mini-lectures" on YouTube by Nassim Taleb.

One ~10 min lecture covered SD and MAD. The other ~6 min lecture covered Fat Tails.

In these ~16 mins, @nntaleb shared so many useful nuggets that I had to write this thread to unpack them.

3/

For those curious, here are the YouTube links to the lectures:

SD and MAD (~10 min): https://t.co/0TwubymdE6

Fat Tails (~6 min):

4/

The first thing to understand is the concept of a Random Variable.

In essence, a Random Variable is a number that depends on a random event.

For example, when we roll a die, we get a Random Variable -- a number from the set {1, 2, 3, 4, 5, 6}.

5/

Every Random Variable has a Probability Distribution.

This tells us all the possible values the Random Variable can take, and their respective probabilities.

For example, when we roll a fair die, we get a Random Variable with this Probability Distribution:
DS
Santiago
Santiago
@svpino
I sat next to one of the smartest, most successful software engineers in the country for over 10 years.

Here's what I learned.

Fast is 10 times better than good.

9 out of 10 times, a "good enough" solution is all you need to unlock time, money, and attention.

Focus relentlessly on being first. Then take the time to be right.

Fast, then right.

Ship more frequently.

The faster you show your work to your users, the faster you'll hear their feedback, you'll adapt, and you'll ship again.

A question you should ask every day:

What can I do to ship this one day before planned?

Technical debt is a good thing.

Everyone hates technical debt. I think they don't know how to take advantage of it.

When used appropriately, technical debt means working on what truly matters and deferring anything that can wait.

No debt ⟶ You waited too long.

Ask, don't think.

People burn their time every day instead of asking for help.

You don't get extra points for working harder, especially when a little help will make you faster.

Time is your most valuable resource. Don't waste it.
DS
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Sahil Sharma
Sahil Sharma
@sahil_vi
@dixit_37 Yup, autocorrect. 😅
DS
Santiago
Santiago
@svpino
10 machine learning questions you should be able to answer.

I started experimenting with a questionnaire before interviewing candidates. Applicants submit a video or written answers to apply for the position.

Thread: Let's look at 10 of these questions.

The questionnaire solves a couple of problems:

1. Weeds out candidates that aren't ready for the job.

2. It significantly cuts down the time we spend talking synchronously.

One problem: It requires extra time from the candidate before the phone interview.

Here are some numbers on a job posting before using the questionnaire:

• 249 applicants
• 13 good applications

That's only 5%.

This sucks.

Numbers after using the questionnaire:

• 44 applicants
• 21 good applications

That's 47%.

This is much better.

I look at it this way:

Sooner or later, I'll ask these questions, and the candidate will have to answer them.

I'd rather give them space and the opportunity to think about their answers without any pressure.

Those who can't or don't want to answer won't apply. That's fine.
DS
Japakusum
Japakusum
@Japakusum1
@paraschopra It's a matter of time before AI realizes futility of existence and merged in the state of Nirvana 😊

People might be worried about the The Age of Ultron type of AI.
I feel that a wisdom seeking AI will quickly pursue Nirvana and go back to the origin of all origins.

An episode of Justice League Unlimited animated series covers it
DS

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Pat Walls
Pat Walls
@thepatwalls
And here they are...

THE WINNERS OF THE 24 HOUR STARTUP CHALLENGE

Remember, this money is just fun. If you launched a product (or even attempted a launch) - you did something worth MUCH more than $1,000.

#24hrstartup

The winners 👇

#10

Lattes For Change - Skip a latte and save a life.

https://t.co/M75RAirZzs

@frantzfries built a platform where you can see how skipping your morning latte could do for the world.

A great product for a great cause.

Congrats Chris on winning $250!


#9

Instaland - Create amazing landing pages for your followers.

https://t.co/5KkveJTAsy

A team project! @bpmct and @BaileyPumfleet built a tool for social media influencers to create simple "swipe up" landing pages for followers.

Really impressive for 24 hours. Congrats!


#8

SayHenlo - Chat without distractions

https://t.co/og0B7gmkW6

Built by @DaltonEdwards, it's a platform for combatting conversation overload. This product was also coded exclusively from an iPad 😲

Dalton is a beast. I'm so excited he placed in the top 10.


#7

CoderStory - Learn to code from developers across the globe!

https://t.co/86Ay6nF4AY

Built by @jesswallaceuk, the project is focused on highlighting the experience of developers and people learning to code.

I wish this existed when I learned to code! Congrats on $250!!
MAKERS
Jaya_Upadhyaya
Jaya_Upadhyaya
@Jayalko1
SRI CHAMPAKADHAMA SWAMY, BANNERGHATTA HILL, BENGALURU (KAR)
#bharatmandir

The shrine is dedicated to Sri Vishnu along with Devi Lakshmi and Bhoodevi.
It is built in an area full of Champaka trees.
As champaka flowers are offered to Sri Vishnu here, it is called Champakadhama.


It is believed that King Janmejaya had sarpa-dosha. To get rid of it, he took bath in the nearby pond. He emerged with a golden body and sarpa-dosha was also removed. The pond is called Swarnamukhi and believed to have curative powers.
Sri Vishnu murti was consecrated by him.


It is also believed that this forest area was the Dandakaranya where Sri Ram stayed in exile.
Some believe that the original shrine was built by Pandavas.
The pushkarni/pond is emptied once a year so that the rock at the bottom (which has image of Anjaneya) may be worshipped.
ALL
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STEALTH JEFF
STEALTH JEFF
@drawandstrike
So it's now October 10, 2018 and....Rod Rosenstein is STILL not fired.

He's STILL in charge of the Mueller investigation.

He's STILL refusing to hand over the McCabe memos.

He's STILL holding up the declassification of the #SpyGate documents & their release to the public.

I love a good cover story.......

The guy had a face-to-face with El Grande Trumpo himself on Air Force One just 2 days ago. Inside just about the most secure SCIF in the world.

And Trump came out of AF1 and gave ol' Rod a big thumbs up!

And so we're right back to 'that dirty rat Rosenstein!' 2 days later.

At this point it's clear some members of Congress are either in on this and helping the cover story or they haven't got a clue and are out in the cold.

Note the conflicting stories about 'Rosenstein cancelled meeting with Congress on Oct 11!"

First, rumors surfaced of a scheduled meeting on Oct. 11 between Rosenstein & members of Congress, and Rosenstein just cancelled it.
ALL
Dr Catherine Pemble is #SocialDistancing
Dr Catherine Pemble is...
@CatherinePemble
Right, screw it. Here's a thread on *that* FBLive by Alex Cairnie about #Govanhill. Lets do a watch-through shall we? (Mind, I'm a resident of Govanhill, and a sociologist. I'm also 5'3, and a visibly queer woman so if half his claims are true I should be living in terror)

00:30: Welcomes viewers and uses points out that his videos are only getting attention b/c they're 'horrific'. Directs folk to his Youtube page. So already clout hunting.

1:08: "I've been warned to stay out of Govanhill. I'm a marked man" (proceeds to use a beanie as a disguise)

1:30 "Laughing and joking aside, here's somebodys front garden." Proceeds to show litter and claim its thrown from windows and this is 'common'. I know a handful of streets with litter issues like this, but its neither common nor unique to #govanhill and other deprived areas.

2:10 "Don't be under any illusion I'm only showing you the bad bits" - only shows you the bad bits.

3:45: He's had offers of help, but hes said no because hes not responsible for 'anybody elses safety' Good lord.

5:01: "Its not that bad. But its not all good either" -Correct

6:00: TW: child abuse - He starts claiming to have been contacted by a health professional and told about child abuse 'in the area'. Huge ethical issues here as the 'professional' who has 'been in the game a long time' is reporting to Facebook Dude and not authorities.
HEALTH
Steve
Steve
@braidedmanga
Ivor Cummins has been wrong (or lying) almost entirely throughout this pandemic and got paid handsomly for it.

He has been wrong (or lying) so often that it will be nearly impossible for me to track every grift, lie, deceit, manipulation he has pulled. I will use...


... other sources who have been trying to shine on light on this grifter (as I have tried to do, time and again:


Example #1: "Still not seeing Sweden signal versus Denmark really"... There it was (Images attached).
19 to 80 is an over 300% difference.

Tweet: https://t.co/36FnYnsRT9


Example #2 - "Yes, I'm comparing the Noridcs / No, you cannot compare the Nordics."

I wonder why...

Tweets: https://t.co/XLfoX4rpck / https://t.co/vjE1ctLU5x


Example #3 - "I'm only looking at what makes the data fit in my favour" a.k.a moving the goalposts.

Tweets: https://t.co/vcDpTu3qyj / https://t.co/CA3N6hC2Lq
FINANCE , ALL