BC DS

Saved by @AnmolNarula11

Santiago
@svpino 3 years, 5 months ago 452 views

Save to PDF Share See On Twitter

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
@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

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.

Advertisement

Santiago
@svpino

A surprising situation.

A machine learning model predicting 3 classes ends up with these results:

• Class A: 90% accuracy
• Class B: 80% accuracy
• Class C: 70% accuracy

Class C seems to be the worst-performing.

But you know already that I'll say it isn't. ↓

Here is the correct answer: We don't know yet which class is the worst.

This is important to understand, so keep reading.

Many people make this same mistake every day and try to fix things that ain't broken.

We are missing something, so let's go and get it.

How about if we ask a friend to look at the dataset manually?

Let's say these are the results of our friend doing the job:

• Class A: 95% accuracy
• Class B: 100% accuracy
• Class C: 71% accuracy

Do you see what just happened?

The model is actually really accurate when predicting Class C! 70% versus 71% when done by a human.

Class B, however, is a disaster: 80% versus 100% when done manually.

Class B seems to be the worse performing class!

The work that our friend did is called a "baseline."

Without having it, it's hard to determine how good are the results of a model.

I've seen tons of money down a drain just because teams didn't create a baseline beforehand.

But now, that won't happen to you 😉.

Santiago
@svpino

I'll blow your mind with a technique you aren't using yet.

Sometimes, you want your system to do exactly the opposite of what your machine learning model thinks you should do.

Let me convince you. ↓

I'm going to start with a nice problem:

Imagine a model that looks at a picture of an electrical transformer and predicts whether it's about to break or not.

Don't worry about how the model does this. We are going to focus on the results instead.

There are 4 possible results for this model:

1. It predicts a bad unit as bad.
2. It predicts a bad unit as good.
3. It predicts a good unit as bad.
4. It predicts a good unit as good.

#2 and #3 are the mistakes the model makes.

Assuming we run 100 units through the model, we can organize the results in a matrix:

• The rows represent the "actual" condition of the transformer.

• The columns represent the "prediction" of the model.

We call this a "Confusion Matrix."

This is how we can read this confusion matrix:

• 60 bad units were predicted as bad.
• 3 bad units were predicted as good.
• 7 good units were predicted as bad.
• 30 good units were predicted as good.

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.

More from Ds

Sahil Sharma
@sahil_vi

🧵🧵🧵🧵 👇

Artificial Inteligence = 🖥️ + 🧠

When can you expect your fridge & mobile phone to revolt against you? (Spoiler Alert: Not in the foreseeable future).

Please Do 're-tweet' the Thread & help me spread the knowledge to more people.

1. The Language
In order to understand Artificial intelligence, machine learning, deep learning, we need to start at the very basics.

Humans speak and understand a wide variety of 'natural' languages (this is what computer science community calls them): like English, Hindi, Marathi, Bengali, Tamil etc.

But have you seen a peculiarity of human languages? They do not have a precise meaning associated with a given sentence. On an average, every English word has 2 meanings. This makes human conversations confusing, with the need for multiple turns & clarifications. Example 👇:

Person 1 conversing with Person 2 face to face:

Person 1: Lets go watch The Dark Knight. Its our marriage anniversary!
Person 2: I dont like going out at night. Its too scary & depressing.
Person 1: Oh, what i meant was watching the movie "The dark Knight".

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

Advertisement

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:

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.

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.

You May Also Like

Max Fagin
@MaxFagin

November is here, and that means a massive shift is coming. And by "massive" I am of course referring to the redefinition of the kilogram unit of mass that the world has been building up to for more than 100 years. Let me explain:

1/ I've had an unhealthy fascination with metrology (the study of measurement) ever since my 2nd year as a physics major when I took a class devoted to duplicating historic physics experiments, so please indulge me for going into heavy detail (get it?) about the kilogram.

2/ So what actually *defines* a unit of measurement? If you're American, you probably know a mile is 5280 feet and a foot is 12 inches and an inch is 2.54 centimeters etc. But where does this chain of definitions end? Is it turtles all the way down?

3/ It's actually not! For all units (even the imperial units used in America) the answers all end with the Système International (SI) unit definitions established and maintained for over 100 years by the Bureau International des Poids et Mesures (BIMP) in France.

4/ At the base of this tower are the SI base units. Just 7 SI base units define every other unit in existence. They are:

Kilogram, kg (mass)
Meter, m (distance)
Second, s (time)
Kelvin, K (temp)
Ampere, A (electric current)
Candela, cd (luminous intensity)
Mole, mol (quantity)

Fidato
@tequieremos

A thread on the life and achievements of Muhiyudin Sayyid Abdul Qadir Jillani RadiAllah who served Islam infinitely as the Reviver (Mujadid) of the 11th Century!

Muhiyudin Sayyid Abdul Qadir Jilani (RA) was one of the greatest Islamic Saint, Scholar, and Jurist. The honorific Muhiyudin denotes his status with many Sufis as a "reviver of religion". Jilani refers to his place of birth. He also carried the epithet Baghdadi.

[1]

Sheikh Abdul Qadir Jilani was born on the eve of the 2nd of Ramadan 470 AH. His birth was a great blessing for the Ummah. It was the arrival of the Sultan al-Awliya (King of the Awliya) of that era, which had been foretold for centuries before his birth.

[2]

When Hazrat Abdul Qadir Jillani was asked by someone what brought him to his high spiritual level, he said, The truthfulness which I promised to my mother. He related the following story:
"One day, on the eve of Eid al-Adhā, I went to my mother, who was then a widow and..

[3]

..and requested her, 'Send me to the path of Truth, give me permission to go to Baghdad to acquire knowledge, to be with the wise and those who are close to Allah. She cried, but she brought out 80 pieces of gold, which was all that my father had left as inheritance.

[4]

Advertisement

$Michael King \U0001f1fa\U0001f1f8$

Michael King 🇺🇸...
@8MichaelKing

Ever notice how the top niches are centered around health, wealth, and relationships?

There's a deep-rooted psychological (and biological) reason why.

And today, I'm gonna to explain all of it so you can start profiting off of this knowledge 💰💰

*Now, this thread is going to be long and in depth. Make sure to favorite the top tweet now so you can come back to it later.

So. Down to the very core of every living being (humans, dolphins, bacteria, aliens), there are only two drivers:
1. To survive
2. To reproduce

Right off the bat, we know that sex is a primary driver to human behavior.

This would also mean that each gender has its own insecurities regarding sex.

Men have a subconscious fear that their woman will leave them for someone more successful

Women have a fear that their man will leave them for someone younger & better-looking

We can conclude that men are attracted to looks, and women are attracted to status.

This is human nature down to its very core.

Crazy, blue-haired feminists will try to argue and say otherwise.

But, 50 years of feminism DOES NOT OVERRIDE millions of years of evolutionary biology.

Here's a quick visualization exercise so you can see how true all this is...

Factualist
@FactualistCom

Trending news of The Rock's daughter Simone Johnson's announcing her new Stage Name is breaking our Versus tool because "Wrestling Name" isn't in our database!

Here's the most useful #Factualist comparison pages #Thread 🧵

What is the difference between “pseudonym” and “stage name?”

Pseudonym means “a fictitious name (more literally, a false name), as those used by writers and movie stars,” while stage name is “the pseudonym of an entertainer.”

https://t.co/hT5XPkTepy #english #wiki #wikidiff

People also found this comparison helpful:

Alias #versus Stage Name: What’s the difference?

Alias means “another name; an assumed name,” while stage name means “the pseudonym of an entertainer.”

https://t.co/Kf7uVKekMd #Etymology #words

Another common #question:

What is the difference between “alias” and “pseudonym?”

As nouns alias means “another name; an assumed name,” while pseudonym means “a fictitious name (more literally, a false name), as those used by writers and movie

Here is a very basic #comparison: "Name versus Stage Name"

As #nouns, the difference is that name means “any nounal word or phrase which indicates a particular person, place, class, or thing,” but stage name means “the pseudonym of an

$Vibhu Vashisth \U0001f1ee\U0001f1f3$

Vibhu Vashisth 🇮🇳...
@VIBHU_Tweet

MAHARAJA CHHATRASAL BUNDELA was the great Bundela King who revolted against the Mughal ruler Aurangzeb.
Chhatrasal Bundela was born in year 1649 CE in Maur Hill of Bundela region to Champat Rai and Lal Kunwar. He was the descendent of Rudra Pratap of Orchha.

His ancesters were the vassals of Mughal Emperor. He drew inspiration from his father and revolted against the alien rule of Mughals. He was just 12 yrs old when his parents died in fight against the Mughal Army of Aurangzeb in 1661.

When his beloved father attained martyrdom in the year 166, the young Chatrasal was present in the last battle of his father against the Mughal and from these valiant campaigns,..

..he gathered the courage to free Bundelkhand from Aurangzeb’s rule.
He joined the Mughals initially, and was recommended by the Rajput King Mirza Jai Singh to be recruited in the Mughal Army in order to support their Deccan Campaigns.

But serving the alien rule of Mughals which brought chaos and misery to Bundelkhand was unacceptable for Chatrasal Bundela and his father’s incessant struggle against the Mughals kept the fire of independent Bundelkhand alive in his heart.