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.
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.
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.
• 249 applicants
• 13 good applications
That's only 5%.
This sucks.
• 44 applicants
• 21 good applications
That's 47%.
This is much better.
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.
They will fool the questionnaire, not the interview that will come later.
Remember:
• The goal of the questionnaire and the resume is just to decide who moves to an interview.
I want to allow candidates to think creatively and elaborate on their answers.
Preferred: A video response with the answers. Fallback is written answers if the candidate is not comfortable with video.
These cover basic principles that we want to ensure candidates understand.
Every candidate receives the same questions.
Let's go over them:
2. Walk us through the process you follow to select the best machine learning algorithm to solve a problem.
3. Explain the trade-off between bias and variance.
5. How do you handle imbalanced datasets in a classification problem?
6. Explain how you would approach reducing overfitting on a model.
8. What would you expect to see as you vary the batch size when training a neural network?
10. What are the advantages of a Convolutional Neural Network over a fully connected network for image classification?
Follow along for a good bunch of practical tips and epic stories about my experience with machine learning.
More from Santiago
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
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 Ds
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:
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:
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1/“What would need to be true for you to….X”
Why is this the most powerful question you can ask when attempting to reach an agreement with another human being or organization?
A thread, co-written by @deanmbrody:
2/ First, “X” could be lots of things. Examples: What would need to be true for you to
- “Feel it's in our best interest for me to be CMO"
- “Feel that we’re in a good place as a company”
- “Feel that we’re on the same page”
- “Feel that we both got what we wanted from this deal
3/ Normally, we aren’t that direct. Example from startup/VC land:
Founders leave VC meetings thinking that every VC will invest, but they rarely do.
Worse over, the founders don’t know what they need to do in order to be fundable.
4/ So why should you ask the magic Q?
To get clarity.
You want to know where you stand, and what it takes to get what you want in a way that also gets them what they want.
It also holds them (mentally) accountable once the thing they need becomes true.
5/ Staying in the context of soliciting investors, the question is “what would need to be true for you to want to invest (or partner with us on this journey, etc)?”
Multiple responses to this question are likely to deliver a positive result.
Why is this the most powerful question you can ask when attempting to reach an agreement with another human being or organization?
A thread, co-written by @deanmbrody:
Next level tactic when closing a sale, candidate, or investment:
— Erik Torenberg (@eriktorenberg) February 27, 2018
Ask: \u201cWhat needs to be true for you to be all in?\u201d
You'll usually get an explicit answer that you might not get otherwise. It also holds them accountable once the thing they need becomes true.
2/ First, “X” could be lots of things. Examples: What would need to be true for you to
- “Feel it's in our best interest for me to be CMO"
- “Feel that we’re in a good place as a company”
- “Feel that we’re on the same page”
- “Feel that we both got what we wanted from this deal
3/ Normally, we aren’t that direct. Example from startup/VC land:
Founders leave VC meetings thinking that every VC will invest, but they rarely do.
Worse over, the founders don’t know what they need to do in order to be fundable.
4/ So why should you ask the magic Q?
To get clarity.
You want to know where you stand, and what it takes to get what you want in a way that also gets them what they want.
It also holds them (mentally) accountable once the thing they need becomes true.
5/ Staying in the context of soliciting investors, the question is “what would need to be true for you to want to invest (or partner with us on this journey, etc)?”
Multiple responses to this question are likely to deliver a positive result.
A brief analysis and comparison of the CSS for Twitter's PWA vs Twitter's legacy desktop website. The difference is dramatic and I'll touch on some reasons why.
Legacy site *downloads* ~630 KB CSS per theme and writing direction.
6,769 rules
9,252 selectors
16.7k declarations
3,370 unique declarations
44 media queries
36 unique colors
50 unique background colors
46 unique font sizes
39 unique z-indices
https://t.co/qyl4Bt1i5x
PWA *incrementally generates* ~30 KB CSS that handles all themes and writing directions.
735 rules
740 selectors
757 declarations
730 unique declarations
0 media queries
11 unique colors
32 unique background colors
15 unique font sizes
7 unique z-indices
https://t.co/w7oNG5KUkJ
The legacy site's CSS is what happens when hundreds of people directly write CSS over many years. Specificity wars, redundancy, a house of cards that can't be fixed. The result is extremely inefficient and error-prone styling that punishes users and developers.
The PWA's CSS is generated on-demand by a JS framework that manages styles and outputs "atomic CSS". The framework can enforce strict constraints and perform optimisations, which is why the CSS is so much smaller and safer. Style conflicts and unbounded CSS growth are avoided.
Legacy site *downloads* ~630 KB CSS per theme and writing direction.
6,769 rules
9,252 selectors
16.7k declarations
3,370 unique declarations
44 media queries
36 unique colors
50 unique background colors
46 unique font sizes
39 unique z-indices
https://t.co/qyl4Bt1i5x
PWA *incrementally generates* ~30 KB CSS that handles all themes and writing directions.
735 rules
740 selectors
757 declarations
730 unique declarations
0 media queries
11 unique colors
32 unique background colors
15 unique font sizes
7 unique z-indices
https://t.co/w7oNG5KUkJ
The legacy site's CSS is what happens when hundreds of people directly write CSS over many years. Specificity wars, redundancy, a house of cards that can't be fixed. The result is extremely inefficient and error-prone styling that punishes users and developers.
The PWA's CSS is generated on-demand by a JS framework that manages styles and outputs "atomic CSS". The framework can enforce strict constraints and perform optimisations, which is why the CSS is so much smaller and safer. Style conflicts and unbounded CSS growth are avoided.
THE MEANING, SIGNIFICANCE AND HISTORY OF SWASTIK
The Swastik is a geometrical figure and an ancient religious icon. Swastik has been Sanatan Dharma’s symbol of auspiciousness – mangalya since time immemorial.
The name swastika comes from Sanskrit (Devanagari: स्वस्तिक, pronounced: swastik) &denotes “conducive to wellbeing or auspicious”.
The word Swastik has a definite etymological origin in Sanskrit. It is derived from the roots su – meaning “well or auspicious” & as meaning “being”.
"सु अस्ति येन तत स्वस्तिकं"
Swastik is de symbol through which everything auspicios occurs
Scholars believe word’s origin in Vedas,known as Swasti mantra;
"🕉स्वस्ति ना इन्द्रो वृधश्रवाहा
स्वस्ति ना पूषा विश्ववेदाहा
स्वस्तिनास्तरक्ष्यो अरिश्तनेमिही
स्वस्तिनो बृहस्पतिर्दधातु"
It translates to," O famed Indra, redeem us. O Pusha, the beholder of all knowledge, redeem us. Redeem us O Garudji, of limitless speed and O Bruhaspati, redeem us".
SWASTIK’s COSMIC ORIGIN
The Swastika represents the living creation in the whole Cosmos.
Hindu astronomers divide the ecliptic circle of cosmos in 27 divisions called https://t.co/sLeuV1R2eQ this manner a cross forms in 4 directions in the celestial sky. At centre of this cross is Dhruva(Polestar). In a line from Dhruva, the stars known as Saptarishi can be observed.
The Swastik is a geometrical figure and an ancient religious icon. Swastik has been Sanatan Dharma’s symbol of auspiciousness – mangalya since time immemorial.
The name swastika comes from Sanskrit (Devanagari: स्वस्तिक, pronounced: swastik) &denotes “conducive to wellbeing or auspicious”.
The word Swastik has a definite etymological origin in Sanskrit. It is derived from the roots su – meaning “well or auspicious” & as meaning “being”.
"सु अस्ति येन तत स्वस्तिकं"
Swastik is de symbol through which everything auspicios occurs
Scholars believe word’s origin in Vedas,known as Swasti mantra;
"🕉स्वस्ति ना इन्द्रो वृधश्रवाहा
स्वस्ति ना पूषा विश्ववेदाहा
स्वस्तिनास्तरक्ष्यो अरिश्तनेमिही
स्वस्तिनो बृहस्पतिर्दधातु"
It translates to," O famed Indra, redeem us. O Pusha, the beholder of all knowledge, redeem us. Redeem us O Garudji, of limitless speed and O Bruhaspati, redeem us".
SWASTIK’s COSMIC ORIGIN
The Swastika represents the living creation in the whole Cosmos.
Hindu astronomers divide the ecliptic circle of cosmos in 27 divisions called https://t.co/sLeuV1R2eQ this manner a cross forms in 4 directions in the celestial sky. At centre of this cross is Dhruva(Polestar). In a line from Dhruva, the stars known as Saptarishi can be observed.
