BC DS
Saved by @AnmolNarula11

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.
On the same topic:

There aren't stupid questions. Only stupid people that don't ask them.

"He who asks a question is a fool for five minutes; he who does not ask a question remains a fool forever." — Chinese Proverb
Communication > technical skills.

You won't get far unless you can convince people, share your ideas, and make connections.

Invest twice as much time learning how to talk and write than how to program.
Not everything you can do is worth doing.

If you want to make a difference, learn to prioritize and delegate.

Invaluable skills:

• Recognizing high-impact work
• Recognizing what wastes your time
• Learning to delegate effectively
Learn all about learning.

You need a process that helps you learn efficiently and effectively.

I wrote an article with my 5-step process to learn new things. You can now go and make it yours:

https://t.co/jYw7WGy6Ht
Share what you learn.

Many people think that hiding information makes them a linchpin. 99% of the time, the opposite happens.

People want to be around those who lift them.

The quickest path to becoming the angular stone of your team: share your knowledge indiscriminately.
Take full responsibility.

A question you should ask every time a problem happens: "What can I do differently next time?"

Look inward. Every time, no matter what.

Finding justifications is easy. It's also a sure way to stay mediocre.
The best code is the one nobody wrote.

Code is a liability. Learn to solve problems by writing as few lines as possible.

An underappreciated superpower: No-code solutions.

Your worst enemy: complexity.
If you don’t test, it doesn’t work.

Any code that can break will eventually break. If you don't have automated tests, you'll suffer the consequences.
Embrace failures.

If you don't fail, you don't learn. If you haven't failed yet, you aren't working on interesting problems.

You fail, you adapt, you try again.
Welcome imposter syndrome.

If you feel inadequate, it means you are stretching yourself and growing. You don't know how to do it yet, but you will.

(People who are working on easy, irrelevant things have no reason to doubt themselves.)
I post threads like this every week. You can find them here: @svpino.

Follow along for a good bunch of practical tips and epic stories about my experience in the field.

More from Santiago

Santiago
Santiago
@svpino
10 machine learning YouTube videos.

On libraries, algorithms, and tools.

(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)

🧵👇

1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.

Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.

https://t.co/HqE9yt8TkB


2⃣ The Pandas library is the gold-standard to manipulate structured data.

Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."

https://t.co/aOLh0dcGF5


3⃣ Data visualization is key for anyone practicing machine learning.

Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.

https://t.co/QxjsODI1HB


4⃣ Another trendy data visualization library is Seaborn.

@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.

https://t.co/eAU5NBucbm
MACHINE LEARNING
Santiago
Santiago
@svpino
Here is a simple example of a machine learning model.

I put it together a long time ago, and it was very helpful! I sliced it apart a thousand times until things started to make sense.

It's TensorFlow and Keras.

If you are starting out, this may be a good puzzle to solve.


The goal of this model is to learn to multiply one-digit
TECH
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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
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.
ALL
Santiago
Santiago
@svpino
11 short programming problems to stretch your imagination and make sure you are staying on your toes.

(Starting with the simple ones, they get more fun as you move towards the end.)

🧵👇

1. Write a function that reverses an array in place.

In other words, the function should not use an auxiliary array to do the work.

2. Write a function that finds the missing number in an unsorted array containing every one of the other 99 numbers ranging from 1 to 100.

3. Write a function that finds the duplicate number in an unsorted array containing every number from 1 to 100.

Only one of the numbers will appear twice.

4. Write a function that removes every duplicate value in an array.

There could be more than one value duplicated. You should remove all of them leaving a single copy of the value.
TECH

More from 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
Sahil Sharma
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".
GENERICLEARNINGS , DS , AI
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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
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
Santiago
Santiago
@svpino
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.
DS

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शमशान में जब महर्षि दधीचि के मांसपिंड का दाह संस्कार हो रहा था तो उनकी पत्नी अपने पति का वियोग सहन नहीं कर पायी और पास में ही स्थित विशाल पीपल वृक्ष के कोटर में अपने तीन वर्ष के बालक को रख के स्वयं चिता पे बैठ कर सती हो गयी ।इस प्रकार ऋषी दधीचि और उनकी पत्नी की मुक्ति हो गयी।


परन्तु पीपल के कोटर में रखा बालक भूख प्यास से तड़पने लगा। जब कुछ नहीं मिला तो वो कोटर में पड़े पीपल के गोदों (फल) को खाकर बड़ा होने लगा। कालान्तर में पीपल के फलों और पत्तों को खाकर बालक का जीवन किसी प्रकार सुरक्षित रहा।

एक दिन देवर्षि नारद वहां से गुजर रहे थे ।नारद ने पीपल के कोटर में बालक को देख कर उसका परिचय मांगा -
नारद बोले - बालक तुम कौन हो?
बालक - यही तो मैं भी जानना चहता हूँ ।
नारद - तुम्हारे जनक कौन हैं?
बालक - यही तो मैं भी जानना चाहता हूँ ।

तब नारद ने आँखें बन्द कर ध्यान लगाया ।


तत्पश्चात आश्चर्यचकित हो कर बालक को बताया कि 'हे बालक! तुम महान दानी महर्षि दधीचि के पुत्र हो । तुम्हारे पिता की अस्थियों का वज्रास्त्र बनाकर ही देवताओं ने असुरों पर विजय पायी थी।तुम्हारे पिता की मृत्यु मात्र 31 वर्ष की वय में ही हो गयी थी'।

बालक - मेरे पिता की अकाल मृत्यु का क्या कारण था?
नारद - तुम्हारे पिता पर शनिदेव की महादशा थी।
बालक - मेरे उपर आयी विपत्ति का कारण क्या था?
नारद - शनिदेव की महादशा।
इतना बताकर देवर्षि नारद ने पीपल के पत्तों और गोदों को खाकर बड़े हुए उस बालक का नाम पिप्पलाद रखा और उसे दीक्षित किया।
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dan spaceman
dan spaceman
@ohlaika
ugh i wanted to do a thread about the wars in Star Wars and how the two outlying wars (the clone wars in the prequels and the cold war/civil war in the sequels) are really weird but i don't feel like it any more.

my first argument is that the Clone Wars were a really complex morally gray moment in Star Wars canon in like, 5 different directions and I'd kind of like to know why Lucas did that.

especially with the direct line that's drawn in the OT with the allegory of WW2 and Vietnam with the rebels being the Vietnamese and the Empire being America. That...doesn't exist in the Clone Wars.

for starters: it's weird it's called the Clone Wars. I get why, because that's what Kenobi calls it in STAR WARS, but the clones are a small facet in the larger story of the war.

the clones basically only exist as a macguffin in AOTC that is...extremely confusing ultimately. Because if you accept that Sidious has this grand master plan to use this war to destabilize the Republic and form an Empire...it's way too complex.
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Stephen Bush
@stephenkb
Some quick thoughts on Sam Gyimah's resignation after conversations with various Conservative MPs: 1) the chances of a second referendum are a lot higher than I thought:

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3) Really just to reiterate 1) Gyimah is the kind of "No, never gonna rebel" vaguely pro-EU Tory you'd need to get on side to outweigh Labour Leavers and Real Concerners.
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Best resort on the Ganga at Rishikesh
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