Sign up when you’re not 100% ready

I booked a ticket to the US earlier this year.

I was supposed to leave on July 6th. My return ticket was for six months later. It was meant to be one-way but I heard somewhere they wouldn’t let me into the US if I didn’t have a return ticket.

In my head it was a one-way trip.

Driving Uber wasn’t an option anymore. It was time to put what I had been studying about machine learning and artificial intelligence to the test.

My thought process was ‘I’ll go to the US and find a job at a tech company with the skills I’ve been learning.’

That was it.

The same reason you go to France to learn French, I was going to go to the US to learn more about technology.

At least that’s what I would tell people who asked.

‘What are your plans when you get over there?’

‘I’ll go to Meetups and meet people and then find a job somehow.’

‘Good on you.’

The real reason was because I didn’t think I could a role here. And really, I hadn’t done any research on what was going on in Brisbane nor did I think anywhere would hire me for skills I learned online.

All I thought was, ‘go to the US with the right mindset and everything will work out.’

That’s how I approach most of life.

Worst case scenario, Australia isn’t a bad place to have to come back home to. It’s a good place here.

So I booked the ticket. Then a couple weeks later Ashley reached out to me on LinkedIn. She said I should meet Mike. So I did. And Mike introduced to me Cam. Two weeks later, I was working at Max Kelsen as a Machine Learning Engineer.

Woah.

Sometimes things happen faster than you expect.

Ashley reached out because I’d been posting some of what I’d been learning on LinkedIn. When I first started sharing my work, I was scared. ‘No one will appreciate this.’ But I kept doing it. And then it happened.

At the time, it seemed unexpected. Looking back, maybe my subconscious knew something would happen if I kept going. I’m glad I did. You can only connect the dots looking backwards.

A few weeks into working at Max Kelsen, Ryan, one of the co-founders, and I were running around a park with stomachs full of pizza.

‘How can I organise a leave request?’ I asked.

‘You can do it through Xero,’ he said.

I explained to him about my flight. It was two weeks away.

‘I’d like to keep the flight but push it back a couple of months.’

‘That’s cool, yeah definitely keep the flight, how about we sit down on Monday and work out some times.’

We decided on September. I’d be leaving Brisbane on September 12 except this time my return flight was 4 weeks later instead of 6 months.

My focus for the trip changed from looking for a job to exploring possibilities. Same same but different.

I went to Japan in 2016 alone for three weeks. Me, my backpack and curiosity as my tour guide. It was one of the best things I ever did.

The US would be no different. Same backpack, same camera bag, same tour guide.

The night before my flight I stayed up late. I wanted to try and combat jet lag. I always pack at the last minute. Mostly because I don’t take many things. Give me a laptop, a toothbrush and a few changes of underwear and I’m good.

My parents took me to airport, my best friend Dave showed up too. We had tea and said our goodbyes. It’s not really a goodbye anymore. Having the internet meant we’d be in contact a few hours later. Anyway.

15-hours later the plane hit the ground in LA. The optimist in me thought 2-hours would be enough to get a connecting flight to San Francisco. Despite running a kilometre in thongs from Terminal 3 to 7 at LAX, it wasn’t.

I knew something was up when the self check-in terminal gave me an error.

‘Excuse me, I can’t check into my flight.’

‘That’s because it’s in 15-minutes, would you like me to rebook one for you?’

15-minutes? ‘That’s enough time,’ I thought, ‘I can still make it.’

It wasn’t.

‘Yes please.’

‘The next one is in an hour, I’ll update your details.’

‘Thank you.’

I made it to San Francisco, bought a SIM card, plugged back into the matrix and brushed my teeth in a public bathroom. Very sleep deprived but I had clean teeth. I was good.

I got some coffee. They had cold brew on tap. Apparently it’s really high in caffeine. It almost got me back to baseline.

On the plane, I drafted out an email to send out for the month of September. It talked about the talk Athon and I did at UQ on AI a couple of weeks prior.

In between sips of cold brew I cut out all the unnecessary words from the brain dump on the flight.

When I got the email to do the talk, I was scared. ‘How could I do this?’

Who was I to give a talk on AI to a travelling group of Chinese Academics? I’d only been studying the stuff for a year.

My rule of having to do something if it scares me got me again. I said yes to the email. That was the on the Friday night, the talk was scheduled for Monday.

I treated signing up for the talk like buying a plan ticket for it US. I wasn’t 100% ready, but I did it anyway.

We spent the weekend researching the topic we were going to talk about. Most of the knowledge was there, it was about bringing it all together in a narrative we could present.

Then we did the talk. And the attendees rated it as ‘excellent’.

The same thing happened with travelling to the US. I’d been spending my whole life preparing to travel alone. Following my curiousity as much as possible and meeting cool people along the way. The only hard part was taking the leap to get there. The rest would take care of itself.

And when I got home and people asked how my trip was, I replied with, ‘excellent.’

 

1. Sign up when you’re not 100% ready

I held off posting on LinkedIn because I didn’t think my thoughts were worthy.

I was waiting for them to be perfect. A clean 100%.

But they never will be.

70% is a better number. A little over halfway but still in the realm of ‘I’m not sure if this will work.’ That’s the sweet spot.

Don’t let being 100% ready stop you from getting after something you’re interested in. Because there’s no such thing as being 100% ready.


2. Do your research

Instead of letting myself give in to the limiting belief of thinking I wasn’t good enough for a job in Australia, I should’ve done my research.

And then maybe I would’ve found the wealth of opportunities not only here but everywhere.

Sometimes to find what you’re after, all you have to do is look.


3. Trust your knowledge

Turns out I already kind of knew there were opportunities a plenty.

But I didn’t trust my knowledge enough to believe I could take them on.

You probably don’t know as much as you think you do. But you also probably know more than you think you do. It’s funny how it works.

If you’ve been putting in the work to build up your skills. Trust them. Admit when you don’t know something but for the rest of the time, let them do their thing.


Arriving in the US

Travelling alone is fun. Want to walk down that street?

You can.

Want to spend all day at an art museum and take a nap on the grass afterwards?

You can.

So I did.

How do you learn machine learning?

Agh.

There’s so much going on.

Dozens of papers every week.

New releases from Facebook and Google every week which send the media into a frenzy.

Articles on the web showing the latest and greatest courses you should be doing.

Where to start?

A year ago, I was asking myself the same question. I quit my job to start a web startup with my friends. It failed. But along the way, I was hearing about this machine learning thing.

‘The computer can learn things for you?’ I said to myself. I knew I had to get involved but didn’t know how.

So I made my own curriculum to at least have some structure.

And then yesterday, I received these.

IMG_2063.png

I know role titles don’t account for much but this was a pretty big milestone for me. I set myself a goal and worked towards it.

I’m still working towards it.

You can too.


A) START WITH WHY

All those courses you start but never finished. Why?

All those things you said you’d do but didn’t. Why?

It’s likely you didn’t have a strong enough reason to begin with.

Before you start learning machine learning before you start learning anything. Ask yourself, ‘why?’

‘Why do I want to learn these skills?’

Find a pen and some paper. Then take a walk. Alone.

Sit down at a nice quiet place.

Take the pen to the top of the page.

‘I want to learn machine learning because...’

Then write down everything that comes to mind.

‘I want a better salary.’

‘I want to build apps to help others.’

‘The technology fascinates me.’

‘I want to be a part of building the future.’

Anything goes. There’s no right or wrong answer. Write it all down.

Got a full page? Good.

Now read back over them. Are they enough to keep you going for the next 10-years?

Why 10-years? Because why sign up to anything if you’re not willing to commit to it?

If they’re not enough to keep you going for 10-years, find something else to learn.

If they are, tape the piece of paper to your wall. You’ve now got your why.

Every time studying gets hard or you think you’re not good enough, refer back to your why.


B) LONG TERM

So. Many. Papers.

I study this stuff every day. And I still can’t keep up.

It takes me 6-hours to properly read a paper. That’s less than 10 a week.

Every couple of weeks there’s a new benchmark. A new way of doing things. A new model to try. A new network architecture which does 1% better than the last.

It’s easy to get lost paying attention to all of this. It can be daunting.

‘Why can’t I ever keep up with what’s going on?’

You shouldn’t not ever pay attention to what’s new. It’s important to know how the field is progressing. But to think you’ll be able to keep up with every single detail, especially when you’re starting out is stupid.

Trying to keep up with everything new will hold you back.

Think long term. The main drivers of machine learning have been around for decades. These aren’t going away anytime soon.

  • Programming (Python, R, Java) - Pick a language and stick with it. Personally, I’m a Python fan but it’s because I’ve never tried anything else.
  • Mathematics (linear algebra, calculus, matrix manipulation, optimisation, statistics) - The highest math education I’ve had is high school since then, I’ve been using Khan Academy to improve my skills.
  • Computing (local hardware, cloud computing, parallelism) - The cloud is abstracting away much of what you need to know about this but if you’re starting out, at least figure out how to activate a computer on the cloud.
  • Communication (what do you know and how can that benefit others?) - Communication is valuable everywhere. Your skills are no good if you can’t explain them to the person next to you.

Work on building a foundation of knowledge and skills around these topics. Then when something new comes out, you’ve got a solid platform to launch off.

Don’t expect to happen overnight. Getting good at these skills takes time and effort. Think months and years not days and weeks. There’s a reason machine learning skills are in demand.

When it gets hard, and it will refer back to A.


C) TRADITIONAL VS. NON-TRADITIONAL

Ron and I were looking up a Bachelor of Mathematics yesterday. 3-years with a major in Applied Mathematics and a minor in Bioinformatics — I love health.

The majority of my learning has been online but after spending a few months in the industry I’ve caught the learning bug.

I’m hungry to learn more. And math is everywhere. It’s the language of nature.

I graduated in 2015 with a Bachelor of Science, Dual Major in Food Science and Nutrition.

My why is to combine my love for health and technology. I don’t know how yet but it’ll come.

A few months after graduating, I went to an open day at my university.

‘How much is a Masters of Computer Science?’ I asked.

‘$42,000 per year,’ she said, ‘with a half payment up front.’

‘What can I study?’

She handed me the brochure.

I read through it and didn’t understand half the course names.

‘Thanks,’ I said.

I went home and consulted my mentor. Google is always there for me.

I typed ‘learn programming online’ or something of the like in the search bar.

Back came some results. I clicked on the first one.

It was Udacity. I spent over an hour looking through their offerings. The pretty colours lured me in.

Seeing all the things I could learn online was exciting. And all for far less than what I was quoted at the open day. I closed the browser.

Then it was a while later and we were building a website and I needed to learn something. Up came Udacity again. There were more offerings. This time with an entertaining character describing deep learning. Siraj Raval knows how to educate and entertain at the same time — the best way to teach.

I signed up for Udacity’s Deep Learning Nanodegree. It started in 3-weeks but I’d never done any Python. So I signed up for Treehouse’s Python Track.

It was hard but my why was strong enough. I was driving Uber on the weekends to pay for my courses. I had two whys. Combine technology and health and stop driving Uber.

After finishing the Deep Learning Nanodegree, I was a little lost. So I went back to my mentor and asked for some guidance. I really liked Udacity’s style of teaching so I found similar courses online and combined them to make my own AI Masters Degree.

I’m still going with it. It’s been a great challenge.

What’s the advantage of studying online?

  • It’s fulfilling to set out your own path and work through it.
  • It’s cheaper than university.
  • You can study when you want — no compulsory lectures.
  • You can study anywhere you want — no longer do you have to physically be at the location to learn from the best in the world.
  • You can learn exactly what you want — course not living up to your expectations? Choose another.

‘Alright, Daniel, all that sounds great but what are the downsides?’

  • Many hours spent alone in your room — it can get lonely sometimes.
  • No one is going to check up on you — you’re it.
  • No shiny certificate at the end — sure, digital certificates are great but real degrees still have more prestige.

To work around the downside of studying alone, I started sharing my journey online. So I’d find more people like me. And I found them. It’s great talking to people on a similar path. We help each other.

I’m a big promoter of online study because it’s what I’ve done. And the educational resources available there are growing every day.

But are you better off going down a more traditional route?

It took me 5-years to do a 3-year degree. I failed my first 2-years. I figured I’d spent enough time at university this decade.

Choose which is best for you.

Does your why require you to have a degree?

Would it help you reach your goals?

Or can you achieve what you want to achieve without going to university?

If you want to get into machine learning research and pursue a PhD program, university is probably the best for you.

If you learn best around a cohort of others learning the same things as you, university is probably the right option.

If you’re self-disciplined, can learn things on your own, and can’t afford university, you’ve got the internet.

What I really did during my first 5-years at university was to learn how to learn. Now I feel like I can learn anything.

All it takes is sustained effort over time.

There’s no best way to learn something other than the way that keeps you wanting more.

For reference, here are some of the courses I’ve done.

You can see more in my full curriculum.


D) SHOW DON’T TELL

I knew my method of learning wouldn’t be recognised as much as more traditional options.

I had to do something to showcase where I could bring value.

I’ll never be the best engineer. But where I can bring value is by being a translator.

The communicative bridge between engineers and customers.

To get skin in the game, I started making videos about what I was learning. And writing articles on the same.

Be honest with yourself. And not only about your weaknesses. It’s easy to be your own biggest critique. Be honest about your strengths too. And show them, don’t just tell people about them.

If you’re a great engineer, show your code, explain it to others.

If you’re brilliant at math, write an article breaking down a proof.

My cards say machine learning engineer but if I think about where I can bring the most value in the future, it’ll be as the front man. I’ll help the people smarter than me communicate how their work can benefit the world.

Not good at anything yet? Show that too.

It’ll help you figure out what you need to improve on.

Show don’t tell.


E) BE THE DUMBEST

When people introduced themselves in the Deep Learning Slack channel it scared me.

‘Hi everyone, I’m Paul, I’m a machine learning engineer at Google.’

‘Hello, my name is Sandra, I’m a software developer at Big Software Company & Co.’

Here I was 3-weeks into learning Python.

‘How am I going to ask a question?’ I thought to myself, ‘everyone is going think I’m dumb!’

Then I realised, being the dumbest in the room meant I could learn the most. If you’re the smartest person in the room, you’re in the wrong room.

I was in a room the other day with one of the lead Genomics researchers in the world. We’re doing a joint project with her team. And she told us, ‘the only stupid question is the one that isn’t asked.’

Don’t be like I was and afraid to ask ‘stupid’ questions.


F) DRIVER VS. MECHANIC

When you drive to the store, how often do you think about how much torque the engine is producing?

Or the amount of air flowing through it?

Or how the fuel pump delivers fuel to the combustion chamber?

A car can be useful to you without this knowledge.

However, if you want to be a mechanic, knowing about these things is a good idea.

The same goes for machine learning. You can learn how to use programming frameworks and libraries such as TensorFlow to build things which are useful without actually having to know what’s going on under the hood.

But if you want to be able to make your networks train faster, work better on different devices, be more computationally efficient, you’re going to have to get some grease on your jacket.

In the beginning, focus on being a driver. Get yourself around. Like driving from one store to the next, solve one machine learning problem after another.

When you’re comfortable driving the car you have, it’s time to upgrade, pop open the hood and dig a little deeper. Practice on a new problem you’ve never seen before. One where you don’t what the answer should look like.

The best thing? If you mess something up you can always reverse the code. There isn’t version control for car engines yet.


G) NON-STOP

The learning never stops.

Every day is day one. When people ask what I do, I still reply with, ‘I’m a student.’

What’s the alternative?

Keep the same knowledge for the next few decades?

No thanks.

The more you learn, the more you realise you don’t know.

My maths skills aren’t as good as they could be.

My statistics knowledge isn’t top notch.

My programming is mediocre at best.

Communication is pretty good. But there’s always room for improvement.

All the real benefits in life come from compound interest. Especially knowledge. But it’s hard to realise it in the moment.

You could study all weekend and no one would notice or care. But you would. You refer back to your why.

Lay one knowledge brick a day and your future self with thank you for the beautiful library you’ve built.


H) TL;DR

1. Start with why. Write your reasons down and make them concrete. When it gets hard. And it will. Refer back to that piece of paper you stuck to your wall.

2. In the beginning, focus on long-term knowledge gains rather than the flavour of the month.

3. Do you need that university degree? Or can you get the skills you’re after without it?

4. Showcase your work. Even if it’s bad. Explain why you did what you did. And how others can avoid your mistakes. Even the best products won’t sell if they don’t have any shelf space.

5. Ask questions. Find people who have been through what you’re going through and ask them for advice. You don’t have to listen to it all, remix it with your own and make it better.

6. You don’t have to be a mechanic to drive a car. Take your new knowledge for a test drive as soon as possible. Don’t let it sit there going to waste in the archives of your brain.

7. The learning stops when your heart does. Keep learning.


I) BONUS

When I first started as a machine learning engineer, what we worked on day to day was very different to what I had in my head.

It’s not always running super sophisticated models on giant computers in the cloud. That’s only the tip of the iceberg.

I made a video showcasing some of the things we get up to on Monday’s at Max Kelsen. Monday’s are research days, where we try things that might not work.

If you’ve got any questions, reach out anytime. You’ll find me everywhere online.

Back to looking more into that Bachelor of Mathematics. I’ll let you know if I end up doing it.

Source: http://qr.ae/TUNima