BC TECH

Saved by @SteveeRogerr

Santiago
@svpino 4 years, 3 months ago 561 views

Save to PDF Share See On Twitter

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.

Focus on the final goal, and don't worry too much about how you get there.

Great results will get you farther than processes, but good processes can help you achieve good results.

Be generous with your knowledge.

It's funny how everything you share finds a way to reward you back.

Who you are is more important than who you were.

We all make mistakes. Move on from them and focus on what's coming.

There are no stupid questions.

Ask away!

(There are, however, stupid people with fragile egos that get bothered when others ask. Ignore them.)

Change is the only thing you can always count on. (And death, and taxes, of course.)

Embrace change.

Better is not always best.

People fantasize about perfection, but perfectionism rarely wins.

Shipping more often will give you better odds than gilding the lily.

Learn to say no.

(I can't say this loud enough!)

Be gracious, professional, nice, but say it more.

Make a habit out of learning.

What you know today will be outdated tomorrow.

Make a plan to keep up and follow it... or you'll get behind.

Focus on one thing at a time.

Multi-tasking murders productivity.

(And turn your phone off!)

Job-hoping may be great for your bank account, but it does nothing to improve the impact you make in the world.

(And it looks horrible in your resume.)

Ideas are worth shit. Execution is worth everything.

Don't be the "idea person". Be the one that takes them and runs with them.

Good communication is a fundamental asset.

You can never invest too much in improving it.

(It doesn't matter how technically good you are if you can't properly communicate with others.)

More from Santiago

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

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.

Advertisement

Santiago
@svpino

I told everyone that I didn't care.

"Screw math! I've never been great with it, so I'm not starting with machine learning to fail at the end."

That was many years ago.

Math is still hard, but I don't think you should be scared at all. Here is why: 🧵👇

[1] One thing changed my mind: school pushed me to the deep end of the pool, and I was forced to swim.

I had to face my fears, and I started machine learning and realized that the math involved is not as scary (or as much) as some people believe.

👇

[2] Probably one of the most frequent questions I get is around the math needed for machine learning.

Answer:

▫️ Probabilities and Statistics
▫️ Linear Algebra
▫️ Calculus

But it turns out that this is not helpful.

👇

[3] What are you gonna do with this?

Are you going to learn all there's is about linear algebra before looking into machine learning?

How about probabilities and statistics? Or calculus? Do you know how extensive calculus is?

👇

[4] Many people stop looking into machine learning as soon as somebody tells them that calculus is a pre-requisite.

First of all, the amount of calculus you truly need is minuscule.

Second, without proper direction, there's no way you'll find your way through the noise.

👇

Santiago
@svpino

One of the most popular activation functions used in deep learning models is ReLU.

I asked: "Is ReLU continuous and differentiable?"

Surprisingly, a lot of people were confused about this.

Let's break this down step by step: ↓

Let's start by defining ReLU:

f(x) = max(0, x)

In English: if x <= 0, the function will return 0. Otherwise, the function will return x.

If you draw this function, you'll get the attached chart.

Notice there are no discontinuities in the function.

This should be enough to answer half of the original question: the ReLU function is continuous.

Let's now think about the differentiable part.

A necessary condition for a function to be differentiable: it must be continuous.

ReLU is continuous. That's good, but not enough.

Its derivative should also exist for every individual point.

Here is where things get interesting.

We can compute the derivative of a function using the attached formula.

(I'm not going to explain where this is coming from; you can trust me on this one.)

We can use this formula to see whether ReLU is differentiable.

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.

More from Tech

ashkan soltani
@ashk4n

BREAKING: @CommonsCMS @DamianCollins just released previously sealed #Six4Three @Facebook documents:

Some random interesting tidbits:

1) Zuck approves shutting down platform API access for Twitter's when Vine is released #competition

2) Facebook engineered ways to access user's call history w/o alerting users:

Team considered access to call history considered 'high PR risk' but 'growth team will charge ahead'. @Facebook created upgrade path to access data w/o subjecting users to Android permissions dialogue.

3) The above also confirms @kashhill and other's suspicion that call history was used to improve PYMK (People You May Know) suggestions and newsfeed rankings.

4) Docs also shed more light into @dseetharaman's story on @Facebook monitoring users' @Onavo VPN activity to determine what competitors to mimic or acquire in 2013.

https://t.co/PwiRIL3v9x

Zen Black
@z3nblack

1/ Thread: "A Silicon Valley Ponzi Scheme"

Thanks to @chamath for laying this out in Social Capital's 2018 annual letter.

I've always appreciated his outspokenness.

2/ The hardest thing for most startups today is the path to market: first finding product-market fit & a way to reach customers, then building a ruthless machine to acquire, monetize & retain them.

3/ Because of this, when the VC industry invests capital into fast growing startups today, the plurality (if not majority) of invested capital will go into user acquisition and ad spending, for better or worse— usually worse.

4/ Todays massive venture-backed advertising, sales, and user acquisition playbook has morphed into one that champions growth at any cost.

This is creating a big bill that will soon come due...

5/ Ad impressions and click-throughs are bid up to outrageous prices by startups flush with venture money, and prospective users demand more and more subsidized products to gain their initial attention.

Advertisement

Corey Gwin
@corey_gwin

Great thread full of potential https://t.co/jA8h08M48z ideas! 💡

📸 Social disposable camera app

Create a digital "roll" with friends. Take pictures from the roll. You can't see them until: the roll is used up, a time limit expires, or you get to a specific location.

🎙 Asynch interview platform

Send a guest a video or audio clip asking a question. Guest replies when able with same medium. Repeat. When done, share produced video/podcast.

✅ 24 Hour Startup Checklist/Overlay

A web app to track the progress of your project: ideation, naming, shipping code, launching.

Page can also be a dynamic browser overlay for live streaming.

Have tools for polls, surveys, sharing, launching to various outlets, etc.

👩‍⚖️ Healthcare "Lawyers"

If health insurance screws you over, no one has your back but you. Instead, can pay a small monthly fee to a service to take care of BS like this for you should issues arise.

$Thomas A. Fine \U0001f1fa\U0001f1f8$

Thomas A. Fine 🇺🇸...
@thomasafine

So is Apple's anti-theft security a wonderful boon to users?

Or is it just another evil corporation strengthening it's monopoly power?

I had the misfortune of buying a 2018 Mac Mini on ebay a few days ago. Unfortunately, it had not been wiped, and it's security defaults had never been changed.

This essentially turns it into a very shiny paperweight.

And this is where everybody jumps to blame the victim. "But everybody knows you have to make sure the seller wipes the Mac."

My how the corporate overlords have trained us to be their apologists.

To be clear, I'm a software engineer. Part of my job is supporting Mac users. Where I work, people hold on to their computers for several years generally, and the T2 chips are only a couple of years old, so I hadn't run into this situation at work.

Of course, I'd read about them, but I was confident that whatever condition the computer arrived in, I'd likely be able to deal with it.

Unfortunately I hadn't read up enough to be an expert on their T2 chip and Activation lock.

Lea Kissner
@LeaKissner

Last up in Privacy Tech for #enigma2021, @xchatty speaking about "IMPLEMENTING DIFFERENTIAL PRIVACY FOR THE 2020

Differential privacy was invented in 2006. Seems like a long time but it's not a long time since a fundamental scientific invention. It took longer than that between the invention of public key cryptography and even the first version of SSL.

But even in 2020, we still can't meet user expectations.
* Data users expect consistent data releases
* Some people call synthetic data "fake data" like
"fake news"
* It's not clear what "quality assurance" and "data exploration" means in a DP framework

We just did the 2020 US census
* required to collect it by the constitution
* but required to maintain privacy by law

But that's hard! What if there were 10 people on the block and all the same sex and age? If you posted something like that, then you would know what everyone's sex and age was on the block.

You May Also Like

Vibhu Vashisth
@VIBHU_Tweet

प्राचीन काल में गाधि नामक एक राजा थे।उनकी सत्यवती नाम की एक पुत्री थी।राजा गाधि ने अपनी पुत्री का विवाह महर्षि भृगु के पुत्र से करवा दिया।महर्षि भृगु इस विवाह से बहुत प्रसन्न हुए और उन्होने अपनी पुत्रवधु को आशीर्वाद देकर उसे कोई भी वर मांगने को कहा।

सत्यवती ने महर्षि भृगु से अपने तथा अपनी माता के लिए पुत्र का वरदान मांगा।ये जानकर महर्षि भृगु ने यज्ञ किया और तत्पश्चात सत्यवती और उसकी माता को अलग-अलग प्रकार के दो चरू (यज्ञ के लिए पकाया हुआ अन्न) दिए और कहा कि ऋतु स्नान के बाद तुम्हारी माता पुत्र की इच्छा लेकर पीपल का आलिंगन...

...करें और तुम भी पुत्र की इच्छा लेकर गूलर वृक्ष का आलिंगन करना। आलिंगन करने के बाद चरू का सेवन करना, इससे तुम दोनो को पुत्र प्राप्ति होगी।परंतु मां बेटी के चरू आपस में बदल जाते हैं और ये महर्षि भृगु अपनी दिव्य दृष्टि से देख लेते हैं।

भृगु ऋषि सत्यवती से कहते हैं,"पुत्री तुम्हारा और तुम्हारी माता ने एक दुसरे के चरू खा लिए हैं।इस कारण तुम्हारा पुत्र ब्राह्मण होते हुए भी क्षत्रिय सा आचरण करेगा और तुम्हारी माता का पुत्र क्षत्रिय होकर भी ब्राह्मण सा आचरण करेगा।"
इस पर सत्यवती ने भृगु ऋषि से बड़ी विनती की।

सत्यवती ने कहा,"मुझे आशीर्वाद दें कि मेरा पुत्र ब्राह्मण सा ही आचरण करे।"तब महर्षि ने उसे ये आशीर्वाद दे दिया कि उसका पुत्र ब्राह्मण सा ही आचरण करेगा किन्तु उसका पौत्र क्षत्रियों सा व्यवहार करेगा। सत्यवती का एक पुत्र हुआ जिसका नाम जम्दाग्नि था जो सप्त ऋषियों में से एक हैं।

Dr. Jane Clare Jones
@janeclarejones

So, on the subject of bonkers hyperbolic pretzeling over the Bell judgement, Grace 'destroy books I don't like & make inappropriate jokes about sterilising teenage girls' Lavery has some thoughts.

Tell me why my feminism is wrong Grace.

Oh

Well, if anyone thought the Bell judgment was going to make TRAs reconsider making massively overblown claims with no evidence backed up with nothing but a thick wadge of emotional blackmail.... HAHAHA, no one thought that.

A high court in the UK made a delimited judgment about teenager's ability to consent to puberty blockers. This puts all trans people everywhere in the world at risk.

Because if any human anywhere has any thoughts that deviate in any way from the rote line dictated by

the trans rights movement, this puts all trans people everywhere in mortal danger.

Let's be honest Grace. It doesn't put trans people at risk. It puts trans ideology at risk. Because trans ideology depends on the idea of innate gender identity, and the trans child is the

necessary material evidence of the ontology of gender identity.

That is, children are being medicalised to provide evidence to underwrite adults identities.

Nothing to see here.

Advertisement

Pulp Librarian
@PulpLibrarian

Due to the pandemic you may not have visited your local library in a while. So come with me on a virtual library tour, courtesy of stock photography, to see what we do for a living...

Libraries are of course information resource centres, but in many ways they are so much more. To get the best out of them you need to really know your way around the stacks.

The enquiries desk is normally your first stop in a library, and this is where you will meet The Angry Librarian! Why is she angry? Because you keep asking her stupid questions!

"Are you open?"
"Do you have a toilet?"
"That chair's wobbly!"
"Why isn't it available in audiobook?"
"Someone else is on the computer and that's not fair!"

On and on it goes...

And that's why in the library we insist on silence. It's the only way to stop us swearing at all the idiotic things you ask us. And we've looked up a lot of old swear words: beardsplitter, bescumber, rantallion, smellfungus etc. We're such muckspouts...

Kieran Snyder
@KieranSnyder

1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

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.

$\u2728Anshika Sharma$

✨Anshika Sharma
@Anshi_________

Low-risk, High-return stocks are what we look for relentlessly

But many of them we know but ignore
some of them don't grab financial headlines

Here are 10 low-risk, high return stocks (that we ignore):

What I mean by “high-return”?

These stocks have OUTPERFORMED the market:

💹Chart with upwards trend Since IPO
💹Chart with upwards trend Over the last 5 Years or since IPO

These are all long-term winners that will keep on winning

What do We Mean by “low-risk”?

A combination of:

❣️Great management
❣️Broad moat
❣️Financially strong
❣️High ROCE
❣️Low Disruption/Dilution/Volatility
❣️Consistent Organic Growth
❣️Assorted Customers/Suppliers
❣️Low debt

1-#CDSL

A depository is a big business and will grow continuously, Its a sustainable business
cause "Market hai toh CDSL bhi hai or rahega"

2- #Dixon

High Management Efficiency with a high ROE of 19.30%
Healthy long-term growth as Net Sales has grown by an annual rate of more than 30%
High Institutional Holdings