The future of education is online (+ 5 resources I've been loving)

Not everyone has access to the best colleges in the world. But the internet provides a way for everyone to access the best knowledge in the world.

There are no shortage of learning materials. Only a shortage of willingness to learn.

Even with such great learning resources available, it still takes a dedicated effort to work through them. To build upon and to create with them.

And one of the best ways for knowledge to spread and be useful is if it’s shared.

Here are 5 things which have caught my attention this week:

1. Open-source state-of-the-art conversational AI

Thomas Wolf wrote a great blog post summarising how the HuggingFace team built a competition winning conversational AI.

All done in 250 lines of refactored PyTorch code on GitHub! 🔥

2. Open-source Data Science Degree

The Open Source Society Unversity repository contains pathways you can use to take advantage of the internet to educate yourself.

3. GitHub Learning Lab

I need to get better at GitHub.

It’s a required skill for all developers and coders.

So I've been using the GitHub learning lab, a free training resource from The GitHub Training Team.

4. 30+ deep learning best practices

This forum post from the fast.ai forums collates some of the best tidbits for improving your models.

My favourite is the cyclic learning rate.

5. A neural network recipe from Tesla's AI Lead

Training neural networks can be hard. 

But there are a few things you can do to help.

And Andrej Karpathy has distilled them for you.

My favourite?

Become one with the data.

PS this post is an excerpt from the newsletter I sent out this morning. If you’d like to get more like these delivered to your inbox, sign up for more.

The business of education

Whatever business you’re in, you’re in the education business.

Here’s why.

If people don’t know how or why they should use your product or service, they won’t use it.

A mistake I see viewing different product pages is a lack.

A lack of explanation of what your service does.

A lack of examples of your product in action.

An over balance of features versus benefits.

“How will my product benefit someone who uses it?”

That’s it. That’s what you should be striving to answer.

Most decisions are made to avoid loss.

If we have to choose something we don’t understand, there’s a high potential for loss.

But if there’s a clear benefit, the cash register will ring.

Grow then rest and reflect

The best athletes train hard in short bursts then rest and reflect on their training.

How could they improve it?

Do they need to increase the intensity? Is their nutrition where it should be? Is the recovery adequate?

The best knowledge workers do the same.

They have intense periods of study and learning and then rest and reflect.

Rest may be doing nothing. Reflecting may be teaching or building something with their knowledge.

Like muscles and reflexes, knowledge grows during rest. And like how the best training programs adapt over time, the best minds are always thinking of new ways to learn.

University vs. Studying Online and How to Get Around Smart People

Lukas emailed me asking a few questions. I replied back with some answers and then he dug deeper. He thought about what I said and then wanted to know more.

I replied back to him with some of my thoughts which I tidied up a bit and put below. The headings are the topics Lukas was curious about. This post doesn’t have all the context but I think you’ll find some value out of it.

Hey Lukas,

I’ll answer these how I did the last ones and break them apart a bit.

1. “University/school teaches some stuff that you don’t really need or want”

This is true. But also true of all learning. Whatever resource you choose, you’ll never use all of it. Some knowledge will come from elsewhere, some will vanish into nothing.

The reason learning online is valuable is it gives you the chance to narrow down on what it is you want immediately. University and school take a ‘boil the ocean’ solution because that’s the only valid one for what they offer. Individualised learning hasn’t made its way into traditional education services. I found I learn best when I follow what I’m interested in so I take the approach of learning the most important thing when it’s required. What's most important? It will depend on the project you’re working on.

Whilst this is an ideal approach for me. It’s important to always reflect on practicality. If I’m building a business and all I want to do is follow what I’m interested in, will that always line up with what customers/the market want? Maybe. Maybe not.

Lately, I’ve been taking the concept of time splitting and applying it to most of what I do. A 70/20/10 split I stole from Google.

In essence, 70% on core product/techniques (improving and innovating on existing knowledge), 20% on new ventures (still tied to core product) and 10% on moonshots (things that might not work).

In the case of my core product, it’s learning health and machine learning skills that can be applied immediately. I distil these in a work project/online creation I share with others.

For new ventures, it’s taking the core product skills and then expanding them on things I haven’t yet done, learning a new technique, working on a new project. But still tied to the core pillars of health and technology.

For moonshots, it’s going, ‘where will the world be in 5-10 years and how can I start working on those things now.’ These don’t necessarily have to relate to the core product but mine kind of still are (since the crossover of health, technology and art interests me most). For this, I’ve been playing around with the idea of an augmented reality (AR) coach/doctor. If AR glasses are going to be a thing, how could I build a health coach service which lives in the AR realm and is summoned/ever present to give insights into different aspects of your health? All of this would be of course personalised to the individual.

If you're still on the fence between university and learning on your own. One thing you may want to look into is the ‘2-year self apprenticeship’. I wrote an article about this which will shed some more light. Especially at 20, this would be something I’d highly recommend (I already have to my brothers, who are your age).

Remember, there's no rush. You've got plenty of time. Work hard and enjoy it.

2. “Why math at university versus on your own?”

I mentioned I was thinking of going to university to study mathematics rather than online. Here's why.

I learned Chinese and Japanese throughout 2016. The most helpful thing was being able to practice speaking with other people face to face.

I stopped after a year and have lost most of what I learned.

Why?

Because I don’t use it and don’t need to use it every day. English is 99.999% enough for conversations in Australia and the work I do.

Math is also a language. The language of nature. Being able to speak it and work on it with other people is a great way to accelerate your knowledge.

That isn’t to say you couldn’t do the same online. But put it this way, I would never try to learn another language without practising conversing from day 1.

If you want to learn French, move to France. If you want to learn math, take math classes with other people who speak math.

3. “How do you get physically around smart people?”

Aside from working with a great team or going to university and having a great cohort. Meetups are the number 1 thing for this.

They are weird and awkward and beautiful.

I always feel like a fish out of water there because everyone seems like a genius.

Events related to your field are priceless. They don’t have to be too often either. I’m finding once a month or so as a sound check to be enough.

4. “Which platform was best for opportunities?”

For content partnerships and online business opportunities: YouTube & LinkedIn (I've been approached or partnered with Coursera, A Cloud Guru, DataCamp, educative.io and more).

For career progression: LinkedIn. If I was looking for a job or more business opportunities, I’d be posting and interacting here daily.

For reaching an audience: Medium. Words are powerful. Writing every day is the best habit I have (aside from daily movement and staying healthy).

A tip for creating.

People are interested in two things when they look at content. Being educated and/or being entertained. Bonus points if you can do both but you don’t need to do both. One is suffice.

Especially if you’re doing a 2-year self apprenticeship or some kind of solo learning journey, share your work from day 1. Share what you’re learning and teach others if you can.

Do not expect it to go viral. Do not expect everyone to love it. These aren’t required.

What’s required is for you to continue improving your skills and to continue improving how to communicate said skills.

Over the long term, those two things are what matter.

Let me know if there’s any follow ups.

Great questions.

Best,

Daniel Bourke

This part sucks

At the start you were making plenty of progress.

One problem solved. Then another. And another.

Then something happens. The results dry up. But the effort stays the same.

It happens after the first few months of weight loss and the kilos stop dropping off.

It happens after the first few courses on data science and the knowledge gains slow down.

It happens when you hit the end of all the machine learning models you know how to use.

The dip.

In 2012, I failed statistics. For the second time. The dip.

2013, learning to code, hit the dip, stopped.

2015, competing in bodybuilding, dip, kept going, got on stage, got a taste for the other side.

In 2017, we started a website, AnyGym, the Airbnb of gyms. Then we stopped. It got hard. We hit the dip and gave up.

But the taste remained.

Now every time it gets hard I think about the dip.

I ask a question.

‘Is it worth it to keep going?’

Sometimes it’s not.

To pass the dip of one thing, you have to quit the dip of many other things.

IMG_0145.PNG
Source: https://www.linkedin.com/feed/update/urn:l...

So many people are learning machine learning. What should you do to stand out?

There it was. Podcasts, YouTube, blog posts, machine learning here there changing this changing that changing it all.

I had to learn. I started. Andrew Ng’s Machine Learning course on Coursera. A bunch of blog posts. It was hard but I was hooked. I kept going. But I needed some structure. I put a few courses together in my own AI Masters Degree. I’m still working through it. It won’t finish. The learning never stops.

Never.

You know this. You’ve seen it happening. You’ve seen the blog posts, you’ve seen the Quora answers, you’ve seen the endless papers the papers which are hard to read the good ones which come explained well with code.

Everyone is learning machine learning.

Machine learning is learning everyone.

How do you stand out?

How how how.

A) Start with skills

The ones you know about, math, code, probability, statistics. All of these could take decades to learn well on their own. But decades is too long. Everyone is learning machine learning. You have to stand out from everyone.

There are courses for these things and courses are great. Courses are great to build a foundation.

Read the books, do the courses, structure what you’re learning.

This week I’m practising code for 30-minutes per day. 30-minutes. That’s what I have to do. When I don’t feel like practicing. I’ll remind myself. These are the skills I have to learn. It’ll be yes or no. It’s my responsibility. I’ll do it. Yes.

Why skills?

Because skills are non-negotiable. Every field requires skills. Machine learning is no different.

If you’re coming from zero, spend a few months getting into the practical work of one thing, math, code, statistics, something. My favourite is code, because it’s what the rest come back to.

If you’re already in the field, a few months, a fear years in, reaccess your skills, what needs improving? What are you good at? How could you become the best in the world at it? If you can’t become the best in the world, combine with something else you’re good at and become the best in the world at the crossover.

B) Got skills? Good. Show them.

Ignore this if you want.

Ignore it and only pay attention to the above. Only pay attention to getting really good at what you’re doing. If you’re the best in the world at what you do, it’s inevitable the world will find out.

What if you aren’t the best in the world yet?

Share your work.

You make a website.

machinelearner.com

I made this up. It might exist.

On your website you share what you’ve been up to. You write an article on an intuitive interpretation of gradient descent. There’s code there and there’s math there. You’ve been working on your skills so to give back you share what you’ve learned in a way others can understand.

The code tab links to your GitHub. On your GitHub you’ve got examples of different algorithms and comments around them and a tutorial on exploratory data analysis of a public health dataset since your interest in health. You’ve ingested a few recent papers and tried to apply it to something.

LinkedIn is your resume, you’ve listed your education, your contributions to different projects the porjects you’ve built the ones you’ve worked on. Every so often you share an update of your latest progress. This week I worked on adding in some new functions to my health project.

You’re getting a bit of traction but it’s time to step it up. You’re after the machine learning role at Airbnb. Their website is so well designed you stayed at their listings you’re a fan of what the work they do you know you could bring them value with your machine learning skills.

You make another website.

whyairbnbshouldhiremeasamachinelearningengineer.com

I made this one up too. Kudos if you’re already on it.

You send it to a few people on the Airbnb recruitment team you found on LinkedIn with a message.

Hi, my name is Charlie, I hope this finds you well.

I’ve seen the Machine Learning Engineer role on your careers page and I’d like to apply.

I made this website which shows my solutions to some of your current challenges.

If you check it out, I’d love your advice on what best to do next.

5/6 of the people you message click on it. This is where they see what you’ve done. You built a recommendation engine. It runs live in the browser. It uses your machine learning skills. Airbnb need a machine learning engineer who has experience with recommendation engines. They recommend a few things.

3 reply with next steps of what to do. The other 2 refer to other people.

How many other people sent through a website showcasing their skills?

0.

Maybe you don’t want a job. Maybe you want to research. Maybe you want to get into a university. The same principles apply.

Get good at what you do. Really good.

Share your work.

How much?

80% skills.

20% sharing.

Source: https://qr.ae/TWpAS2

The “2 Year Self Apprenticeship”

My friend sent me this post. 

The ‘2 Year Self Apprenticeship’ model by @lewismocker

The ‘2 Year Self Apprenticeship’ model by @lewismocker

Reading it was a form of confirmation bias. It was as if I was reading what I’d been subconsciously (or consciously? How do you tell?) doing the past 2-years.

I’m in between step 4 and 5. 

It started with creating my own AI Masters Degree. That turned into a job as a machine learning engineer. And the creating hasn’t stopped. Publishing work online has opened more doors for more me than any of my previous ventures.

I haven’t figured out 6 yet. But it’ll come. In the meantime, I’ll keep making.

Enough about me. What can you take away from this?

The post already says enough. I won’t repeat any of it. But I can add a lesson or two.

A) Choosing yourself is hard but worth it

It’s not for everyone. The traditional paths are there for a reason. They’ve stood the test of time. They work for some but not for others. 

When I was younger I thought I’d be a TV star one day. My mum took me to an audition for an advertisement company. I was nervous but I liked being the centre of attention. After the audition we never heard back. Dreams shattered.

Then one day my mum found out the company went broke. I was 10. 10-year-olds don’t understand companies going broke. Why wasn’t I going to be a TV star?

Everywhere I went I felt like a combination of special and the one who didn’t fit in. I liked that. Maybe everyone feels it? Probably.

Aghh. Enough about me. That’s a 2 count. 

When you pick your own path, you’ll have people questioning what you’re doing. You’ll get advice from all angles.

But there will be something inside of you telling you to push forward. You can’t explain it. When you try to tell someone else, they might get it, they might not. All the advice they give comes from a kind place but they’re not in your head. They don’t have to lay in bed at night with your thoughts. They don’t have to sit down at lunchtime and stare out the window with the feeling in your gut of the thing that’s pulling you. 

Then you do it. You make the decision you’ve had sitting in your brain your body your soul. And it happens. The whole universe starts getting behind you. But it doesn’t make it any easier. You’ll keep coming up against obstacles keep questioning.

Is this the right thing? 

Will things work out?

Where’s the answer? 

Yes, maybe, no, it doesn’t exist, all valid answers. 

Choosing yourself is a daily practice. You make the decision. Then you follow up with the effort.

Then tomorrow happens. And you repeat. 

B) Online is great but people are better

The internet is amazing. It has lowered the barrier to entry to education, to creating, to making, to sharing, to meeting, to finding. You know this. But it’s not perfect. You know this too. 

You can learn from the best in the world and then remix their ideas with yours and share them. Others can find your work and learn from it and do the same. The snowball gets bigger. 

The one thing the technology hasn’t replicated yet is the feeling of connection. Online communities are everywhere but they’re not the same as sitting down at a table with like-minded people.

Someone messaged me the other day. ‘Hello Daniel, I’m a self-made XYZ as well.’

The message meant well and I thanked the person for the kind words. But I’m not self-made. There’s no such thing as self-made. 

This one is an asterisk on the end of the ‘2 Year Self Apprenticeship*’.

*Take advantage of the online resources available to you. But don’t forget about your offline relationships.

An offline relationship can be completely online but it takes more than the odd like to convey it. Interact with those who are in your circle. Message the people whose work you enjoy, share it and say why you like it. These kind of acts are what keep the snowball growing. 

Keep learning. Keep making. 

 

 

 

Slow learning, fast

‘Will this guarantee me a role in machine learning?’

‘If I do these courses, will I be job ready?’

‘Are these courses accepted by the industry?’

I responded to some emails this morning asking these questions.

My content may seem as though everything happens fast.

It’s wrong. What gets published is a fraction of what happens in real life.

Learning things takes time. A 10-minute video is often a highlight reel of a week’s worth of effort.

A single article may describe a month worth of different lessons.

One principle I try to follow is to always invest in my myself. Someone smarter than me told me you never go wrong investing in yourself.

When I get up in the morning and I don’t feel like working on the things I have to work on, I ask myself, ‘what’s the alternative?’ Most of the time I know the answer. The alternative to studying is not studying. And that won’t help me learn what I need to learn.

I have to remind myself learning is hard. It’s supposed to be hard. Hard is good. Hard is fun. If everything was too easy, we’d get bored.

Industry will have different opinions on what courses are valid. No role is ever guaranteed. And no course will ever 100% prepare you for a job. But that doesn’t mean all hope is lost.

You can keep building your skills. Skills are more important  than certificates.

You can move fast in the short-term but be patient in the long-term. This means working what you need to work on day in day out and letting it accumulate over time.

You can communicate your skills as a form of validation. Learned something? Prove it by showing your work. People love seeing what others have done. And the ones who don’t are not the ones you’re worried about anyway.

The only thing that’s guaranteed?

Learning never stops.

How do non-technical people learn machine learning?

I drove forward.

The parking inspector starting speaking.

Do you have a valid Queensland drivers licence?

I answered.

Yes.

He kept going.

Well, you shouldn’t because you should know you can’t park in bus stops.

The Uber app guided me to pick up riders. I followed the app without paying attention to the signs. I was more focused on picking them up and getting them out of there. It was 2 am.

The fine came through. $250. I worked for free that night.

I paid it.

Then thought to myself.

I’m not driving Uber anymore.

Two weeks later I got offered an internship as a machine learning engineer.

9-months before that I started my own AI Masters Degree.

Before that, I graduated with a Food Science and Nutrition Degree. Non-technical as it gets.

Where do you start?

A) Delete non-technical from your vocabulary

Words have power. Real power.

They’re magic. It’s why when you list out the letters of a word it’s called spelling.

People isolate themselves with their words.

Some say play to your strengths, others say work on your weaknesses. Both good advice. Which one should you listen to?

As soon as you start saying you’re non-technical, you’re non-technical.

I was speaking to someone the other night.

I used to think my main strength was talking to people.

I told him.

I’ll never be the best engineer.

He snapped back.

Not with that attitude.

It changed me. I’m not trying to be the best engineer but referring to myself as never being the best was limiting my ability to grow.

I’m getting better. Much better. Why?

Because I told myself so.

You can too.

Belief is 50% of anything.

B) Use the placebo effect to your advantage

Here’s another.

Have you heard of the placebo effect?

It’s one of the most dominant forces in science. But it’s not limited to researchers in lab coats. You can use it too.

Example.

People who thought they were taking good medicine (but were actually only taking a placebo, or a sugar pill) got healthier.

What?

Why?

Because they thought they were taking the good medicine and the cosmic forces between the mind, body and universe set them on the track to better health.

I’ve simplified it and used cosmic forces on purpose. Because this effect is still unknown other than describing it as a belief which led to improvement.

What can you do?

The same thing. Take a placebo pill of learning machine learning.

Write it down.

This will be hard for me but I can learn it.

Again.

This will be hard for me but I can learn it.

All useful skills are hard to learn.

C) Get some coding foundations

The first two are most important. The rest snowballs as you go.

Someone commented on my LinkedIn the other night.

One of my favourite sayings from my professor was, "in theory, theory and practice are the same. In practice, they are completely different".

Good advice.

Could you learn to swim without ever touching the water?

If you want to get into machine learning, learn to code, it’s hard to begin with but you get better.

Practice a little every day. And if you miss a day, no problem, continue the next day.

It’s like how your 3-year-old self would’ve learned to talk.

In the beginning, you could only get a few sounds out. A few years later, you can have whole conversations.

Learning to code is the same. It starts out as a foreign language. But then as you learn more, you can start to string things together.

My brother is an accountant. He’s starting to learn machine learning. I recommended he start with Python on DataCamp. Python code reads similar to how you would read words. Plus, DataCamp teaches code from 0 to full-blown machine learning. He's been loving it.

D) Build a framework

Once you’ve been through a few DataCamp courses or learned some Python in general, start to piece together where you want to head next.

This is hard.

Because in the beginning it’s hard to know where you want to go and there’s a bunch of stuff out there.

So you’ve got two problems. Not knowing where to go and having too many things to choose from.

If you know you want to learn more machine learning, why not put together your own path?

What could this look like?

  1. 3–4 months of DataCamp

  2. 3–4 months of Coursera courses

  3. 3–4 months going through the fast.ai curriculum

Do you have to use these?

No.

I only recommend them because I’ve been through them as a part of my AI Masters Degree. The best advice comes from mentors who are 1–3 years ahead of you. Short enough to still remember the specifics and long enough to have made some mistakes.

Will it be easy?

No.

All useful skills are hard to learn.

Day by day you may not feel like you’re learning much. But by the end of the year (3 blocks of 4 months) you’ll be a machine learning practitioner.

E) You don’t need math*

*to get started.

When you look at machine learning resources, many of them have a bunch of math requirements.

Math isn’t taught well in schools so it scares people.

Like code, mathematics is another language. Mathematics is the language of nature.

If the math prerequisites of some of the courses you’ve been looking at are holding you back, you can get started without it.

The Python coding frameworks such as TensorFlow, PyTorch, NumPy and sklearn, abstract away the need to fully understand the math (don’t worry if you don’t know what these are you’ll find them later).

As you go forward and get better at the code, your project may demand knowledge of the math involved. Learn it then.

F) It’s always day one

Am I the best machine learning engineer?

No.

But two years ago I was asking myself the question, how I do learn machine learning with no technical skills?

The answer was simple, start learning the technical skills and don’t stop, but there were details.

Details like above.

Driving Uber on the weekends allowed me to pay for the courses I was doing to learn machine learning.

Getting a fine for picking up people in the wrong spot helped me make the decision to back myself.

A year into being a machine learning engineer and I’m more technical than when I started but there’s plenty more to learn.

6 Tips to Keep Yourself From Getting Distracted Whilst Studying

Tomorrow happens.

I get up. I’m tired. I went to bed late. Distracted by my phone. Sophie was texting me.

Did she reply?

She didn’t. But there’s other red bubbles. More red bubbles. I tap one. Then another. I’ve been up for 46-minutes. I have no idea what I’ve done.

I decide that’s enough phone time for today. It goes in the drawer.

 

A) $1000/hour

If there was $1000 on the table, would you take it?

Yes of course, I would.

How about if someone else was at the table?

They turn to you.

Give me $1000.

Would you give to them?

I wouldn’t.

And if you wouldn’t either, why do you give away your time so easily?

Value your time at $1000 per hour.

“Would I pay $1000 per hour to do this?”

Study and education. Yes.

Random online internet surfing. No.

Seeing those people you knew 6-years ago buy a boat and go fishing with their new friends. No.

The list of things gets shorter real quick.

 

B) Keep the energy bar high

Tomorrow happens again.

My energy bar is already low.

Poor sleep and poor foods over the past couple of days have clogged everything.

My energy. Clogged.

My gut. Clogged.

My brain. Clogged.

How am I supposed to study whilst everything is clogged?

I decide I need to sleep more and eat better and begin at once.

 

C) What the hell am I doing?

A third tomorrow happens.

That’s three yesterdays with nothing. Three nothings.

But my energy bar is full today. My phone is away. I’m ready.

I sit down.

The void consumes me. There’s a pile of this and that and a stack of thoughts. I’m drowning in thoughts. Too much. What the hell am I doing?

I spend 17-minutes trying to decide. I figure it out. I put a few things down. Lost in thought but found in the words.

Math work.

Coding practice.

Reading.

Yeah, that’s good. Three things, that’s enough.

 

D) Time it

Math work got done but none of the others.

I’m starting to get this starting to get the hang of things.

Math work was good. My energy bar was high. Top sleep and food does the trick.

It got me. Got me good. The math. I got lost. Lost in the patterns. I was on a roll. Connecting the dots. Playing.

I lost track of time. These things happen when you’re playing.

Next time I must put a time limit on each one. Not too much. But not too little I can’t do what I need to do.

 

E) Playing

I set up a timer. I want these things done. I’m getting better.

A full energy bar.

Valued time. $1000 per hour!

A list of things to do.

Look at me go!

The timer is running. 25-minutes of playing.

I learned it from the math study.

Work is playing. Studying is playing. I’ve convinced myself. They’re the same. It helps you know. It does.

I think if I can learn this and then that, I’m in a game, I can turn it into a game. Study becomes play.

 

F) Enough

All this time playing I finish exhausted. All of the list done. A depleted energy bar. Thousands of dollars of time and effort dedicated.

I lay in bed. Still intrigued. The best way to be. But I know I must rest. The sleep will help. Help me focus. Keep my energy bar up.

I can it enough. When I’ve reached it, I call it. I’ve done a good job today. There’s more to do though. There’s always more to do.

My list of what to do.

My timer to help with the list. $1000 per hour.

My full energy bar. The sleep and food. And the bending exercises.

My phone away.

These things will help.

I lay in bed. Still intrigued.

Tomorrow happens.

Source: https://www.quora.com/What-are-some-ways-t...

"How do you stay motivated whilst studying?" — Ask a Machine Learning Engineer Anything

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

How I study five days a week

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.

A) Fix your environment

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.

Why?

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.

B) Set the system up so you always win

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.

Aghhhhhhh.

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.

C) Sometimes do nothing

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.

D) Embrace the suck

Studying sucks.

You learn one thing and forget it the next day.

Then another and forget it.

Another.

Forgotten.

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.

E) The 3-year-old principle

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.

It's bedtime.

That's a bonus.

F) Sleep

Poor sleep means poor studying.

Don't trade sleep for more study time. Do the opposite.

Source: https://qr.ae/TUfk19

What to study

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.