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Santiago
@svpino 5 years, 3 months ago 1482 views

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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

More from Santiago

Santiago
@svpino

A topic that comes up in every interview.

Bias, variance, and their relationship with machine learning algorithms. One of the most basic concepts that you have to know by heart.

Here is a simple summary that you will easily remember.

↓

Every machine learning algorithm deals with 3 types of errors:

1. Bias error
2. Variance error
3. Irreducible error

There's nothing we can do about #3.

Let's focus on the other two.

↓ 1/5

"Bias" refers to the assumptions the model makes to simplify the process of finding answers.

The more assumptions it makes, the more biased the model is.

"Variance" refers to how much the answers given by the model will change if we use different training data.

If the answers stay the same regardless of the data, the model has low variance.

Often, linear models are high-bias, and nonlinear models are low-bias.

Example low-bias algorithms:
• Decision Trees
• SVN
• kNN

Example high-bias algorithms:
• Linear Regression
• Logistic Regression

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 😉.

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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

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.

Santiago
@svpino

For a long time, I didn't understand how to use Virtual Environments in Python 🐍.

If this is just, let's end it here and now: 🧵👇

[2] Virtual Environments let you deal with the dependencies that your code has with external Python libraries.

It avoids having conflicts when your projects depend on different versions of the same library.

👇

[3] Let's imagine that you are building your first Python project and you install the "requests" library:

pip install requests

You get version 2.24.0 installed in your system.

👇

[4] A month later, you decide to work on your second project. It also needs the "requests" library.

But the latest version is not 2.24.0 anymore.

Now version 3 is available, and that's the one you want to use!

👇

[5] You could upgrade your entire system to version 3, but then you'll be potentially breaking the first project you built that depends on 2.24.0!

Can you imagine this happening on a server with many more applications running?

👇

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This is my son, Sam. He’s 5. And today he learned how shitty and harmful #ToxicMasculinity is. My rage meter is spiking right now so excuse me if this is a little raw but there are some things I want to say about BS #gender norms (a thread)

Sam is my middle child & he’s a terror. A “boy’s boy” as so many (not me) would say. He’s rough and tumble, he’s loud, he’s always dirty, loves trucks, plays sports and knee drops me from the couch. But he also loves a lot of “girl” things.

Sam has a collection of purses because he likes to carry things around. And he also loves to have his nails painted bright colors because he thinks they “look beautiful.” And he’s right – they are beautiful…

So he proudly wore his red nail polish to kindergarten this morning because Sam has absolutely no concept of nail polish only being for girls or reason to think anyone would possibly have a problem with beautiful nails.

But his classmates did have a problem. A big one. Sam was ridiculed for being a boy with nail polish. They called him names and told him to take it off. This lasted the entire day.

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A Thread on the Boss himself @Mitesh_Engr

Mitesh Sir's Positional Option Selling 101:

• How to find direction
• Which options to sell
• How to deploy capital
• Exit criteria
• What ROI he targets weekly
• What % risk he takes

Done with the help of @niki_poojary

How @Mitesh_Engr Sir finds the direction?

• Daily charts S/R
• 75 min charts S/R
• Intraday trend
• Always play directional
• Never trades in strategies

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• Weekly candle High/Low
• Sell 1% away options from those
• Exit when levels breached

Position Sizing by @Mitesh_Engr Sir.

How to deploy your capital?

• First sell 20%
• Pyramid the next day
• When to exit
• What to do when view goes wrong
• What to do with idle capital

Some important tweets of @Mitesh_Engr Sir

• What Data to look at
• ROI for safe players per month
• Max risk per day
• Gather knowledge and play directional
• Never play strategies

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@AnuSatheesh5

Story of Thumba Poo and Shiva

Thumba poo is a tiny white flower which grows in all areas. These flowers are used for treatment for sinus. Small children who have polyps in Nose are told to crush tumbha leaves and apply in nose part.
There is an interesting story of Thumba and

Mahadeva. Thumba flowers were not taken for Shiva Pooja. Saddened to this Thumba prayed sincerely and Mahadeva appeared before her.
She was surprised that Shiva had appeared before her. Instead of saying that she wants to under his feet always, she said
@SriramKannan77

She said that her feet should be on his head. Shiva accepted her prayers. It is because of this Thumba flower is placed in the head of Shiva Linga.
Shambo Mahadeva
Om Namah Sivaye
🙏🕉🙏

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@SaveToBookmarks Namaste! You can find it here. Original tweet by @AchuthArora: "@AJA_Cortes @buzz_chronicles @rattibha @SaveToBookmarks save as Workouts". Enjoy 👌

Hi! There you go! Original tweet by @SaveToBookmarks: "@AchuthArora @AJA_Cortes @buzz_chronicles @rattibha Cool!

Your tweet has been s...". Cheers! 👉

Ax
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@AJA_Cortes @buzz_chronicles @rattibha @SaveToBookmarks save as Workouts

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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.

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ADI SHANKAR AND SRI TOTAKACHARYA

Adi Shankaracharya chose his disciple Totakacharya to be the first head of the Math at Joshimath.
The Narsingh Mandir (Mandir of Sri Narasimha) in Joshimath is one of the 108 Divyadesams and also the home of Badrinath during the winter months.

Totakacharya was earlier a boy named ‘Giri’ and a devout disciple of Adi Shankaracharya. Unlike others, he was not known to be an expert in Shastras but his dedication to his Guru was complete and he used to listen to His discourses with utmost faith and devotion.
@SriRamya21

One morning as Adi Shankara and his disciples were getting ready for the discourse, Giri was delayed. The other disciples urged their Guru to commence the lesson on Bramhsutra Bhashya telling him that Giri would anyway not be able to comprehend its deep meaning.
@RajeAiyer

Adi Shankar responded “नायमात्मा प्रवचनेन लभ्यो न मेधया” – Atman cannot be attained through discourses or mere intellect.
He,then, awakened the supreme knowledge in Giri.
Giri spontaneously composed: श्री शङ्करदेशिकाष्टकम् extolling his Guru’s grace.

Adi Shankaracharya granted sanyasa to Giri and gave him the name Totakacharya.
The story of how Giri became Totakacharya is a great example of the importance of faith and devotion in your Guru and not the mere knowledge of the Shastras.
@DetheEsha

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@KieranSnyder

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3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.