Santiago Profile picture
18 Jun, 20 tweets, 4 min read
I just went through 200+ applications for a machine learning position.

I think I figured out what works and doesn't in a resume.

Here are a few tips.
The question I want to answer when I read a resume:

• Should I call this person?

Anything that doesn't help me with this decision is working against you.
The most common problem I found when reviewing applications:

Most resumes are 90% noise and 10% signal. People bury relevant details in an ocean of useless information.

Fix this, and you increase your chances significantly.
How can you make your resume 100% on point?

1. Keep your resume down to one page.
2. Tailor your resume to the position.
3. Remove anything that doesn't speak to the job.

Let's break these down.
1. Keep your resume down to one page

Nobody ever looks at the second page of their Google search. The same happens with your resume.

If I'm not sold after reading the first page, nothing will change my mind on the second page.
2. Tailor your resume to the position.

Are you applying for a computer vision position? Center your resume around that.

Don't let the person reading the resume hunt for clues. Everything relevant should be right there on their face.
3. Remove anything that doesn't speak to the job.

The resume is not the place to list your life accomplishments unless they contribute to getting that call.

Connect every line you write to the job opening. If you can't, get rid of it.
With every resume, I found myself looking for a quick way to decide whether the person was a good fit.

A 2-3 sentence summary is your opportunity to shine. Your sales pitch right at the top:

• What are you looking for?
• What are you capable of?
• Why are you good at it?
Your education is important.

But in my opinion, your experience is the most valuable asset you bring to the table.

Experience > Education

Don't start with your certifications, diplomas, and degrees. Start with what you have done and how it is relevant to the job.
Speaking about education:

Keep superfluous details out of your resume. It only adds to the noise, and nobody cares:

• Your GPA
• Your coursework at school
• Anything that happened 10+ years ago
Many people don't read the job post.

Popular advice: "Apply if you meet half the requirements."

I disagree.

If the requirements are stupid, don't apply at all. If they make sense, but you don't meet them, don't apply either.

Spend your time on battles that you can win.
I want to make an exception with job posts that clearly ask for impossible requirements.

Those who ask for 10 years of experience in a framework that's 5 years old.

Most people laugh at these. I think they are an incredible opportunity.
Companies that need a lot of help and are just creating their teams don't know better. They'll copy requirements from here and there and put together a half-assed job post.

If you get this job, you'll start from the ground floor. You'll get to build a team from scratch!
Getting back at people not reading job posts:

The post asks for people with experience in ABC technology.

Yet you mention ABC as your 9th bullet right after 8 completely irrelevant points.

I discard those resumes pretty quickly: they are clearly mass-applying to jobs.
Is mass-applying to jobs a problem?

Not really. But if you do, you won't be able to compete with a resume that's speaking directly to the position.

In my book: Quality > Quantity

(But I understand this looks different at different stages of your career.)
To recap:

• Your goal: Getting a call
• Read the job post
• Tailor your resume
• Cut it down to 1 page
• Sale yourself in 2-3 sentences
• Experience > Education
• No superfluous details
• Quality > Quantity
I post threads like this every week. More than once.

Stay tuned as I help you get to the core of practical machine learning.

You can find the rest of my threads here: @svpino.
The vast majority of applicants came from academia/research positions with no industry experience.

That's fine, as long as you can talk about specific things that you've built during that time.

It's harder when you only have teaching experience.

Suggestion Of The Day: "Use machine learning to solve this."

Right. I get it. Others have tried. I already use as much automation as I'm comfortable with.

This would be a much-pressing issue for a large company that's hiring all the time.

Here is a compromise:

Create 3 versions of your resume with different focuses. Send the best version that matches the job you are applying.

You don’t get the full benefits of a custom tailored resume, but it’s much better than sending a generic resume.

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More from @svpino

17 Jun
Here is a simple trick that improves the results of your models.

Best part: You'll surprise your team. Guaranteed.

Thread: What is Test-Time Augmentation, and how you can start using it today.

Let's start with Data Augmentation:

It allows us to artificially increase the size of a dataset without having to collect more data.

Data augmentation is key to improve the performance of our models.
Data augmentation is very popular when working with images.

We can apply different transformations to each image to obtain new samples:

• Cropping
• Rotations
• Flipping
• Zoom

We can then train the model with the augmented dataset and get much better results.
Read 10 tweets
15 Jun
If you are planning to get started with Machine Learning:

My introductory course is on sale:

• 50 copies @ $5, starting right now.
• Free copies for those who can't afford this.

gum.co/kBjbC/five

If you want to support my content, like/retweet this. Everyone wins.
10 copies sold, 40 copies left @ $5.
20 copies sold, 30 copies left @ $5.
Read 11 tweets
15 Jun
Do you know what's holding you back?

The same boring projects than everyone else is working on. How do you break off the mold and make a difference?

Thread: 7 machine learning projects that will teach you the technical skills you need to succeed out there.
Today, companies are dumping insane amounts of money on people with the right skills.

But here we are, showing up with the same "MNIST Digit Recognition" and "Iris dataset" experience.

These are good to start, but you need to take your learning to another level.
The good news for you: Most people can't be bothered.

Most people will bookmark this thread and will never do anything with it.

Even if you do one of these 7 projects, you'll be doing more than 99% of everyone who reads this!

It has never been easier to stand out!
Read 19 tweets
14 Jun
How close are we to building a truly intelligent agent?

Most scientists think we are still decades away, but today, a group of scientists from @DeepMind claims they know how to get there.

Let's talk about what's going on.
What is "Artificial General Intelligence" (AGI)?

An agent capable of learning any intellectual task than a person can also learn.

Today, AI has been limited to systems that can learn particular tasks. A system that can learn anything you teach it, just like a human, is AGI.
Unfortunately, there's no way to build such a general, intelligent agent without formulating a custom solution for every individual task.

This sucks. This doesn't scale. This doesn't get us to AGI.

But maybe we aren't that far off...
Read 16 tweets
12 Jun
6 lies you have been told about machine learning:

1. You need a lot of math to start
2. You need a Ph.D. to get a job
3. You always need a lot of data
4. You need to buy expensive hardware
5. It's hard to become proficient in it
6. It's the solution for most problems

Bullshit.
In the last 6 months, I've posted more than 100 threads here on Twitter talking about machine learning and how you can build a career on it.

And I'm just getting started!

Stay tuned. A lot more is coming.
First misconception: All machine learning is hardware-hungry.

Deep learning stretches you, but outside that, it gets much better.

If you need GPUs/TPUs, there are many free/cheap options you can use, especially while learning.

Read 9 tweets
11 Jun
Software developers want to get into machine learning.

Many make the same mistakes. I've seen a few, and I have some ideas on how to avoid them.

This is what I've learned ↓
Lesson 1

Most people love the idea of starting something new. Only a few take the first step.

Preparing for something new is fun and exciting. It can also turn into glorified procrastination.

Stop collecting resources. Take what's right in front of you and run with it.
Lesson 2

Learning is a marathon, not a sprint. Strap yourself for a long, lifelong road.

If you are looking to make a quick buck, look elsewhere. If you are looking for shortcuts, this ain't it.

Make sure you come for the right reasons and are willing to go the distance.
Read 12 tweets

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