Manager — “Hey, I need you to do the thing, faster and cheaper than last time.”
Leader — “We’re going to do this, I don’t know how yet but we’ll figure it out. Let’s go.”
Manager — “Hey, I need you to do the thing, faster and cheaper than last time.”
Leader — “We’re going to do this, I don’t know how yet but we’ll figure it out. Let’s go.”
You wake up. You check your phone. Its been on charge all night. The battery usage from the previous day was recorded and will be used along with previous use history to maximise charge. Machine learning.
Your new emails load, there’s an email from Steve. He wants the thing done earlier. It’s always earlier. Faster.
There are a few more emails. One from Amazon. Your package is going to be shipped today. And a new Uber promotion. 10% of rides for this week. You click the link and apply the code.
The rest of the emails are garbage. Stuff you’ve subscribed to but didn’t need. At least you subscribed to them.
There’s another 15 emails you don’t see. They’re in the spam folder. Your email client scanned through the text as they came in and put them there. Machine learning.
It isn’t perfect though. You could’ve been a millionaire. A billionaire! If only you’d seen the email and sent that Nigerian Prince your bank details.
A charge comes through on your card. The amount pops up on your screen. $37.85 from Amazon. Another email. Your order of Machine Learning is Everywhere is on its way.
Another charge. Gym membership for the week.
All this money, flying across the internet. You’d think someone could tap into this stream and take some. They try but they can’t. Even when they get your card. They try to buy something. It might go through the first time but by the second time, the card is dead.
Your bank detected the fraud and froze your account. The transaction in another country wasn’t like the other ones you’ve made in the past.
We’ve detected fraud and frozen your account. Don’t worry, your funds are safe. To sort this out, you can contact us here.
There’s no way any person could monitor all the transactions happening. Machine learning.
You call the bank. Your card has been unfrozen and the funds will be back in your account in 24-hours.
It’s 8:34 am. The Uber app pops up at the bottom of your phone. Your calendar says work starts at 9 and based on previous trips, your phone knows it takes about 16-minutes to get there. Machine learning.
6 drivers are close by. Less than usual. Your work address is already preloaded. You get matched with the Black Prius, licence plate, 889LYJK. Machine learning.
The driver takes a route you’ve never been. And then the map corrects itself to adjust for traffic. Machine learning.
Josh is in Bali. Sarah had a birthday party on the weekend. Their photos are on Instagram. An ad appears for those new shoes you’ve been looking at. The ones with the orange. The ad was delivered in your feed amongst photos from the people you’ve been following — there’s a balance. Too many ads and you’d stop using Instagram. Machine learning.
And the naked picture from HotPix2928133Q in your discover tab? You didn’t see it because it was filtered out. Machine learning.
The traffic is bad. Car, car, car, car, truck, car, bus, car. Nose to tail. The Black Prius crawls ahead. The cameras in the front grill stop it from getting too close to the car in front. Machine learning.
You’re not even at work yet.
Tomorrow is your day off. What’s the weather like? Open the weather app. Sunny with a chance of rain, 18% chance. Where’d the chance come from? Machine learning.
Breakfast: a bagel with bacon and avocado. You’ve been trying to cut the carbs but Bagel Boys do it so well. So well. The bagels are a dream. They’re $7 but you’d pay $10.
You can't see them but they're there. The drones monitoring the wheat crops. They look for crop stress. Too much stress is never good. Too much stress leads to death. The drones help the farmer. They help the farmer so you can taste the Bagel Boys glory. Machine learning.
The address reader knows what characters look like. It reads your address on the package and sends it down the chute. Machine learning.
All the information is encoded in the barcode but not everywhere has the same barcode system. The driver picks up a collection of parcels. Your book is one of them. Your house is close to the depot so it's delivered before 10.
It's a good book. A best seller. You want to tell Ankit it arrived and tell him he should read it too. Your phones screen brightness changes as you take a photo. The photo looks so crisp. You’re a real photographer now. The book is in focus and the background is blurred. All with the tap of a button. Magic? No. Machine learning.
Machine Learning is Everywhere.
Sometimes you need to give up exercise.
And everything else.
And focus on the work.
I was hosting a live question and answer session on my YouTube channel.
Ask a machine learning engineer anything.
Someone asked how to focus. Before I could answer, people started offering some great advice in the chat.
Except for the one about sacrificing exercise to work more. It was from a good place, but I disagreed.
There’s nothing I could work on with is worth sacrificing my health or relationships.
Plus, the work won’t matter if you don’t have your health.
Health is the force multiplier of life.
As much as I love data science, health has my heart.
I answered more ML related questions on the stream.
“How do I get a job in ML?”
“How much math is required?”
“What courses are the best?”
You can watch the full video on YouTube.
Or listen to the audio version. If the player below doesn’t, it’s available on Anchor too.
And if your question didn’t get answered, feel free to ask anytime.
Good can't exist without evil.
Life implies death.
Hard can't exist without easy.
Avoid the hard things all the time or try and cling to good sensations whenever you can.
And you will fail at both.
Every month, I host a livestream on my channel where I answer some of the most common questions I get, plus as many of the live questions as I can.
"How can I get a job in machine learning?”
“Where’s the best place to learn machine learning?”
“How do you manage your time?”
“How do you stay fit whilst studying?”
“What do you think of Coursera, EdX, Udacity and Udemy?”
“Should I go to university to study data science?”Read More
I’m a firm believer the best mentor is someone who is a few years in front of you.
And it has nothing to do with age. It’s got to do with mission.
What do you want to achieve in the next 3-5 years?
If you’re looking for advice, you should look for people who have been through a similar journey in a similar timeframe.
Any longer than a few years in front and the advice gets hazy.
How well do you remember the details of your day-to-day 6-years ago? Or longer?
Over the past 6-7 years, Sam Ovens has built a $30 million per year business. At the time of writing he’s 29. I’m 25.
In his latest video, he shares some of the things he has been thinking about. To make sure they sink in, I’ve summarised them in my own words here.
Before the internet, the balance used to be, spend 80% of your time shouting about how good your product is (sales and marketing) and 20% actually building a good product.
Now, your product is discoverable. If it’s good, people will search for it. People will talk about it.
Spend 80% of your time making your product or service better and 20% telling people about it.
Don’t be confused by page views or likes or any other metric which doesn’t relate to improving your business.
Business school be summed up in one sentence.
“Bring someone else enough value for them to pay you.”
That’s it. That’s all you have to do.
Don’t over complicate it.
If your product or service brings more value to someone than your competition, you will win.
At the start, your business can be all you.
You can be 100% of the talent. You can do 100% of the tasks.
But as you go, if you want to expand, you’ll have to recruit help.
If it’s only you, you’ll be beaten as soon as someone hires a couple of talented people to work on the same thing.
If you want your business to grow beyond solopreneur status, don’t be the only talented person on your team.
Team will always be the most valuable asset you can build.
“I want to hire intelligent, unorthodox, athletes.”
Intelligence is like horsepower. Useless on its own but powerful when applied. Looking for someone with a specific set of skills may be more difficult than finding someone who already has a great foundation of intelligence and then enabling them to apply it.
The world is changing. Always. This is the only guarantee in business. What got you to where you are last year, might not even move the needle next year. Unorthodox people question the status quo. They ask why. They’re not afraid to have strong opinions and back them up. They’re the ones who are willing to try something different.
Business is competition. Even if you tell yourself it isn’t, you’ll be competing against someone. Another business, a changing world or most importantly, yourself. Athletes understand competition. They thrive in it.
Combine these three and you have yourself a recipe for a potential great hire.
Google, Amazon, Facebook, Apple, Microsoft, all the rest. These companies are all looking for the best talent.
If you’re a small business, it can be hard to convince someone to come and work for you to begin with.
Add in the billions of dollars and brand power of the companies above and you’ve got a David vs. Goliath problem.
If your number 1 focus is recruiting a great team, what do you think theirs is?
So how do you win?
In the movie Moneyball, there’s a baseball team who doesn’t have the budget of some of the other teams in the league.
Instead of trying to go for all the best players, the ones with high batting averages and great pitching, they look at the other stats.
Their analyst looks through the league, combing for players who don’t necessarily cut it for the big contracts but are on the fringe.
The team ends up winning a record number of games straight with only a portion the budget of the bigger teams.
Find the people whose talents haven’t yet been fully discovered. Then when they come on board, get out of their way and empower them to use them.
Note: This is hard. Really hard. Hence why if you’re looking to expand your business, you should be dedicating a lot of time to recruitment (80-90% in Sam’s case).
“One of my worst fears is being old and not being able to work and not being able to let the business I’ve built run without me.”
The quote above isn’t word for word. But it’s what I remember.
Thousands of people collaborated to build the pyramids. An effort which spanned decades.
When you’re starting out, your focus should be on short-term cashflow. Earning enough money to keep your business going and growing.
But as you reach a stage where the business can sustain itself without too high of a focus on short-term cash flow, if you want to build something of pyramid status, your focus should shift to the long term.
This all comes back to having a strong team and continually bringing value to the most important people. Your customers.
I find it invaluable to have these kind of lessons being shared so accessibly.
You can watch the full video on Sam Ovens’s YouTube channel.
Many a man have drove themselves into the ground trying to chase after the wrong thing. Including me.
More money than what covers basic needs.
The love of an evasive woman.
Popularity amongst others.
Stacy was a good girl, just not into me. I realised and got out of her house, slammed the door of the Sub and sped down the road. The speed didn’t make anything better.
A man needs to experience his first heartbreak. I don’t trust anyone whose never felt a wrench in their gut at the sound of someone’s name.
Anyway, I tell you these these things because I somehow think it’ll help.
But in my experience, most of the best lessons have to happen first hand, then you can join the dots.
Don’t confuse chasing after the wrong thing as success.
A man only needs a few things to get by. Some food, a mission, a deep love for himself, a place to sleep. Anything else is a bonus.
Keep creating Pauly.
You can have it all.
All the drive in the world.
But you still need gas.
Work hard but rest when you need.
If you’re health starts to fade, it’s your body reminding you to let off the pedal.
Don’t sacrifice health for hustle.
I had no job.
Then I started driving Uber on the weekends to pay for my studies.
I loved meeting new people but I hated driving a car all the time. Traffic, stop, start, fuel, the air, the aircon, all of it.
I studied machine learning. All day, five days a week. And it was hard. It's still hard.
9-months in, I got a job.
It's the best job I've ever had.
Your grandfather’s first orange farm failed.
The soil was good. The seeds were there. All the equipment too. What happened?
It was too cold. Oranges need warm temperatures to grow.
Your grandfather had the skills to grow oranges but there was no chance they were growing in a cold climate.
When he moved to a warmer city, he started another orange farm.
12-months later, your grandfather was serving the best orange juice in town.
Studying is like growing oranges.
You could have a laptop, an internet connection, the best books and still not be motivated to study.
Because your environment is off.
Your room is filled with distractions.
You try to study with friends but they aren’t as dedicated as you.
Whatsapp goes off every 7-minutes.
What can you do?
Clean your room. Find a different study group. Friends are great when it comes to friend time but study time is study time. Put your phone in a drawer for an hour.
Fix your environment and let the knowledge juices flow.
Problem 13 has you stumped. You’re stuck.
You wanted to get it done yesterday but couldn’t.
Now it’s time to study but you know how hard you worked yesterday and got nowhere.
You’re putting it off.
You know you should be doing it.
But you’re putting it off.
It’s a cycle.
The pile of books stares at you. Problem 13.
You set a timer. 25-minutes.
You know you might not solve the problem but you can sit down for 25-minutes and try.
4-minutes in, it’s hell. Burning hell. But you keep going.
24-minutes in and you don’t want to stop.
The timer goes off and you set another. And then another. After 3 sessions, you solve the problem.
You can't always control whether you make progress with study. But you can control how much time you spend on something.
Can control: four 25-minute sessions per day.
Can't control: finishing every task you start every day.
Set the system up so you always win.
I did the Coursera Learning How to Learn course the other day.
One of the main topics was focused versus diffused thinking.
Focused thinking happens when you're doing a single task.
Diffused thinking happens when you're not thinking about anything in particular.
The best learning happens at the crossover of these two.
It's why you have some of your best thoughts in the shower. Because there's nothing else happening.
When you let diffused thinking takeover, it gives your brain space to tie together all of the things it absorbed during focused thinking.
The catch is, for it to work properly, you need time in both.
If you've set the system up so you do four 25-minute sessions of focused work, go for a walk after. Have a nap. Sit and think about what you've learned.
The world could do with more of nothing.
You learn one thing and forget it the next day.
Then another and forget it.
You spend the whole weekend studying, go to work on Monday and no one knows.
Then after a year of studying something you realise how much more there is to still learn.
When will it end?
It doesn't. It's always day one.
Embrace the suck.
I was at the park the other day.
There was a young boy running around having the time of his life.
Up the slide, down the slide, in the tree, out of the tree, in the dirt, out of the dirt, up the hill, down the hill.
He was laughing and jumping then laughing again.
His mum came over to pick him up.
"Come on, Charlie, we've got to go."
He kept laughing as she carried him away, waving his blue plastic shovel.
What is it that fascinated him?
He was playing. He was having fun. The whole world was new.
Our culture has a strict divide between work and play.
Study is seen as work.
You're supposed to study to get more work. You're supposed to work to earn money. The money buys you leisure time. Then and only then can you be like Charlie and run around laughing.
If you have it in your head study is work, it will be hell.
But suppose, you have the idea about it that studying is the process of going through one topic and then to the next.
Connecting different things like a game.
The same feeling about it as you might have as if you were Charlie going down the slide.
You learn one thing, you use it to learn something else, you get stuck, you get over it, you learn another thing. And you make a dance out of it.
Do this and you'll finish a study session with more energy than you started.
This is the 3-year-old principle. Seeing everything as play.
That's enough for now.
That's a bonus.
Poor sleep means poor studying.
Don't trade sleep for more study time. Do the opposite.
She was screaming.
You don’t know what’s happening Gregory!
You just wait Greg, I know what you’re up to!
There isn’t anything I don’t know Gregory!
I walked across the street. What was happening? Why didn’t Greg know? What did she know about Greg? There wasn’t anything she didn’t know. Was she an oracle?
I should’ve talked to her. I could’ve asked her about life. She could’ve help me figure it all out.
I pretended like I did.
Can I ask you something?
There isn’t anything I don’t know Daniel!
What’s the most precious thing you can give to someone else?
Give them a feeling!
Something that penetrates their soul! Get deep! Really deep! Make it bubble up!
Make what bubble up?
You want it to be there in a year! In 5-years! When they’re in a cafe reading a newspaper and they look up and get nauseous thinking of the feeling! You want it to be so good it comes back! It always comes back!
I get it but why are you screaming?
Why do you think! Do you not listen! You’re just like Gregory! You don’t know what’s happening!
Imagining is almost as good as the real thing. Sometimes it’s better. Sometimes worse. Far worse.
She made me feel something. Unintentional or intentional? Who knows. She was talking to Gregory. Gregory didn’t know what was happening.
Her purple top left her stomach uncovered. And the pants she had on were dirty. Maybe she was crazy. Maybe I was crazy for taking lessons imaginary lessons from a screaming lady on the street. Gregory wasn’t. Gregory didn’t know what was happening.
Financial freedom has two extremes. One where you can buy whatever you want. The other where you don’t care about anything. She was financially free. Rich. Rich with the most valuable currency there is. Rich with effect. Effect on others. Effect on me. She had an audience. Everyone crossing the street was in awe. What was Greg up to?
Effect is precious. It can last an instant but be remembered for a lifetime. It can happen once and then again 1000 more times.
You could be sitting in a cafe in 5-years reading the newspaper. Look up for a second. Your stomach does a backflip. And it comes back, the feeling all over again.
How she smiled at you.
Or how she screamed from across the street.
Once you give a feeling to someone, it’s there. Always there.
Is far more important than where to study.
How you learn is more important than how long.
The best teachers are the ones which inspire you to learn more.
The best books are the ones which you don’t want to stop reading.
The internet has provided access to some of the greatest teachers and learning materials.
Now you have the choice of who your teacher is and what you read.
If you try one and don’t like it, you can move on. There will be more information on the topic somewhere else.
“Education is becoming more and more accessible. What’s scarce is a willingness to learn.” — Naval
And a willingness to learn comes with a willingness to be wrong.
Who’s going to use it?
What do they value?
What’s their background?
How have they done things in the past?
What problem do they need solved?
If you’re making something, it helps to think of one person and answer these questions. Then make it for that one person.
Bioinformatics is the crossover of biology, computer science, statistics and math. It’s the discipline of using computational methods to make sense of biological data.Read More
We were hosting a Meetup on robotics in Australia and it was question time.
Someone asked a question.
“How do I get into artificial intelligence and machine learning from a different background?”
Nick turned and called my name.
“Where’s Dan Bourke?”
I was backstage and talking to Alex. I walked over.
“Here he is,” Nick continued, “Dan comes from a health science background, he studied nutrition, then drove Uber, learned machine learning online and has now been with Max Kelsen as a machine learning engineer for going on a year.”
Nick is the CEO and Co-founder of Max Kelsen.
I stood and kept listening.
“He has documented his journey online and if you have any questions, I’m sure he’d be happy to help.”
The questions finished and I went back to the food.
Ankit came over. He told me about the project he was working on to use machine learning to try and understand student learning better. He was combining lecture attendance rates, time spent on the online learning portal, quiz results, plus a few other things. He’d even built a front-end web portal to interact with the results.
Ankit’s work inspired me. It made me want to do better.
Then a few more people started coming over and asking questions about how to get into machine learning. All from different fields.
This is the hard part. I still see myself as a beginner. I am a beginner.
Am I the right mentor?
The best mentor is someone who’s 1-2 years in front of you. Someone who has just been through what you’re about to go through. Any longer and the advice gets fuzzy. You want it when it’s fresh.
My brother is getting into machine learning. Here’s what I’ve been saying to him.
The language doesn’t really matter. It could be R, Java, Python, whatever. What matters is picking one and sticking with it.
If you’re starting out, you’ll find it hard to go wrong with Python.
And if you want to get into applied machine learning, code is compulsory.
Pick a foundations course from online and follow it through for a couple of months. Bonus points if it’s geared towards teaching data science at the same time. DataCamp is great for this.
It’ll get hard at times but that’s the point. Learning a programming language is like learning another language and another way of thinking at the same time.
But you’ve done it before. Remember when you were 3? Probably not. But people all around you were using words and sounds you’d never heard before. Then after a while, you started using them too.
Apply what you’ve learned as soon as you can.
No matter how many courses you’ve completed, you’ll never be 100% ready.
Don’t get lured into completing more courses as a sign of competence.
This is one thing I’d change if I went back and started again.
Find a project of your own to work on and learn through being wrong.
Back to your 3-year-old self. Every 3rd word you said would’ve been wrong. No sentence structure, no grammar either. Everything just came out.
There are plenty of courses out there. All of them great.
It’s hard to find a bad one.
But here’s the thing. Since there are so many, it can be hard to choose. Another trap which can hold you back.
To get around this, I made my own AI Masters Degree. My own custom track to follow.
You can copy it if you want. But I encourage you to spend a few days doing research of your own and seeing what’s best for you.
As a heads up, three resources I’ve found most aligned to what I do day-to-day are, the Hands-On Machine Learning Book, the fastai Machine Learning course and the Applied Data Science with Python course on Coursera.
Bookmark these for after you’ve had a few months Python experience.
You’ll see articles and papers coming out every day about new machine learning methods.
There’s no way to keep up with them all and it’ll only hold you back from getting your foundations set.
Most of the best machine learning techniques have been around for decades. What’s changed has been an increase in computing power and the availability of data.
Don’t be distracted by the new.
If you’re starting out, stick to getting your foundations first. Then expand your knowledge as your project requires.
3-year-old you was a learning machine (a machine learner?).
In a couple of years, you went from no words to talking with people who had been speaking for decades.
Because you practised a little per day.
Then the compound interest kicked in.
1% better every day = 3700% better at the end of the year.
If you miss a day, no matter, life happens. Resume when you can.
Soon enough you’ll start to speak the language of data.
“Have you ever built a recommendation engine?”
“We’ve got a project that requires one as a proof of concept, think you can figure it out?”
Most people think learning stops after high-school or college. It doesn’t.
The scenario above happened the other week. I’d never built a recommendation engine. Then I did.
Failure isn’t bad if you’re failing at something you’ve done before. You’ve been walking your whole life but you don’t beat yourself up when you trip on your own feet. It happens. You keep walking.
But failing at something new is tough. You’ve never done it before.
Learning machine learning kind of goes like this.
1st year: You suck.
2nd year: You're better than the year before but you think you suck even more because you realise how much you don’t know.
3rd year: ???? (I’m not there yet)
Embrace the suck.
How much will beating yourself up for not knowing something help you for learning more?
Learning something new takes time. Every day is day one.
How would your 3-year-old self react to not knowing a word?
You’d laugh. Throw your hands in the air and then crawl around for a bit.
It’s the same now. Except you can walk.