Saying not doing

That’s my problem.

I’ve been saying to myself, saying to others I want to build things.

But I haven’t.

It’s called signalling. Talking about something instead of doing it.

It started with wanting build a YouTube channel.

Then I did it. And it felt good.

I wanted to write. So I did. Everyday. And it makes me happy.

Now I want to build apps. Build products which add value to the world.

Don’t make the same mistakes I did.

If you know what you want to do. Remember.

The fastest way to get where you want to go is to go straight there.

My latest YouTube video talks about two things I’m doing wrong in my business. One of them is signalling. The other is jumping from project to project rather than building something scalable (a product built on code or media).


Iterate and deliver

That’s all you have to remember.

When you first begin and when you keep going.

If you can pull those two off, you’ll be in a good place.

Whether it be making videos, writing articles, building projects, providing a service, pay attention to these two words.

That’s what I’m reminding myself.

My ability to stay in business depends on it.

Yours does too.

My latest video documents my second week running an online business. I also talk about lifetime value. One of the most important yet underestimated business metrics.


Don’t let perfection hold back published

School told me I wasn’t an artist. I believed it. My year 8 art teacher gave me a D for my drawing. All that effort. A green ninja turtle. Not the ones you see on TV, my own.

The next year I dropped art, music and drama. All of it. Anything that wasn’t maths or science.

In year 12 I got a C- for English. The stories I wrote weren’t good. Why not? I thought I ticked the criteria. I read the book, rewrote it in my words, told it how I understood it. Now I couldn’t write, draw, act. What was the deal?

There was one thing though. I was captain of debating. I could speak. Writing differently to speaking bored me. My English essay was good (to me, not the teacher) but it was a drag to write. I had to take time off gaming to get it down. We were the best Call of Duty team in Australia, that’s a full-time job.

Our debating team went to other schools and they came to us. We’d have a week with a topic a week to wrap our 17-year-old brains around it and form an argument, for or against. Sometimes there wasn’t a topic. Instead, we’d get there an hour early and get given the topic on the night. You had to think of a speech on the spot.

I was always third speaker. Which meant I had the job of summarising the first two speakers on our team and saying why the other team was wrong. I loved it.

While the other team were speaking I had to think of why they were wrong and write it down. I didn’t have time to write an essay. I had to write how I was going to speak it. Then I had to deliver. That’s what mattered.

We went through our final year undefeated.

Essays are still hard for me to write and there’s nothing worse than reading poor writing. One of the easiest ways to improve your writing is to write like you speak. If you can’t explain something with words, say it out loud as if you were telling your friend about it and then write that.

It took me 7-years out of school to start creating again. To start writing publicly. To start making videos. I still haven’t gotten back into drawing. But I will.

When it first went live it wasn’t good. My first 30 YouTube videos were me sitting in my car. There were gaming videos on another channel but they weren’t me. My first articles were over edited, ‘what if someone thinks this when they read that?’

Thankfully they’ve gotten better since. And I have no plans to stop improving, stop challenging myself. That’s the key. Be your own biggest critique. Make things you’d like to see and make them quality.

But after a while being your own biggest critique gets easy. Then you have to learn how to be your own biggest fan. Fan and critique at the same time.

I hit 10,000 subscribers the other day. Now we’re on the way to 100,000.

But that’s not the metric I pay attention to. The metrics you can game don’t matter.

What then?

Work published.

Was it published?

Did the idea turn into something?

Where is it?

Can I see it?

Not everything goes out into the world. It shouldn’t. But in order to get better, you have to publish.

That’s what I measure myself on.

It’s what led me to being able to leave my job as a machine learning engineer and pursue a journey on my own. Let’s see where it goes.

If you’ve watched my videos or read my articles. Thank you.

My first contribution to an open source deep learning library

GitHub still confuses me. But it's needed. You can create your own tools but the best come from collaboration.

The philosophy of open source is simple. Take the best information and knowledge from others and make it available to every one in an accessible manner and let them create.

It says, here's the thing we've built, you can use it for free but if you find a way to improve it, let us know but we'd appreciate it if you made the change yourself.

Most open source libraries have far more users than contributors. And that's a good thing. It shows the scalability of software. It means many can benefit from the work of a few.

Since starting to learn machine learning, I've used plenty of open source software but I'd never contributed back. Until now.

We've been working on a text classification problem at Max Kelsen. The model we built was good, really good. But it wasn't perfect. No model is. So we wanted to know what it didn't know.

Our search led to Bayesian methods. I don't have the language to describe them properly but they offer a solution to the problem of figuring out what your model doesn't know.

How?

In our case, we used Monte Carlo dropout to estimate model uncertainty. Monte Carlo dropout removes part of your model every time it makes a prediction. The Monte Carlo part means you end up with 100 (this number can change) different predictions on each sample all made with slightly different versions of your original model. How your 100 predictions vary, indicates how certain or uncertain your model is about a prediction. In the ideal scenario, all 100 would be the same. Where as, 100 different predictions would be considered very uncertain.

Our text classifier was based on the ULMFit architecture using the fast.ai deep learning library. This worked well but the fast.ai library didn't have Monte Carlo dropout built-in. We built it for our problem and it worked well.* Maybe others could find value from it too, so we made a pull request to the fast.ai GitHub repository.

With a few changes from the authors, the code was accepted. Now others can use the code we created.

A contribution to open source doesn't have to be adding new functionality. It could be fixing an error, adding some documentation about something or making existing code run better.

Still stuck?

Best to start with scratching your own itch. You might not have one to begin with, I didn't for 2-years. But now I've done it once, I know what's required for next time.

If you want to learn more, I made a video about the what, why and how of a pull request. And I used the one we made to the fast.ai library as the example.

*After a few more experiments, we've started to question the usefulness of the Monte Carlo dropout method. In short, our thinking is if you simulate different versions of your model enough, eventually you end up with your same model. So the pull request may not be as useful as we originally thought. You have to be skeptical of your own work. Doing so is what makes it better. Stay tuned.

Gradatim Ferociter | Ask a Machine Learning Engineer Anything May 2019

Harsath messaged me a year ago. He was getting into machine learning and data science and had seen some of my videos.

Every time I’d post a new video he’d be one of the first to comment. Something insightful, something kind. I’ve always been grateful to see his name pop up. There’s also Shaik, Hammad, Gregory, Yash, Paul and many more.

This time Harsath told me he got a role in the field. He’d been working hard towards it and was offered a job.

It came after sustained effort over time. Step by step. It reminded me of the saying, gradatim ferociter, it means step ferociously.

Big things rarely happen in one go. It takes many small steps, one at a time. And each step has to be taken with passion and ferocity.

Congratulations Harsath. Keep up the effort and keep learning.

In the May Ask me Anything, I answered your questions around studying online versus at college, how to get a job in the field, having a PhD versus self-taught, using Bayesian methods in machine learning, my intermittent fasting schedule and more.

As always, if you have any more questions, feel free to reach out.


Activity vs. Progress

“Are you making progress or completing activities?” he said, “That’s what I ask myself at the end of each day.”

“I’m writing that down.”

We kept talking. Not much more worth writing down though.

“Let me know what you get up to.”

“Okay, I will.”

“Talk soon.”

“Have a good day mate. Goodbye.”

Too many activities can feel like progress. That’s what he was talking about. You could be working yourself to the bone but the list never gets any smaller.

Maybe it’s time to get a new list.

One which leads to progress instead of a whole bunch of activities being checked off at the end of the day.

I catch myself when I’m writing a list each morning. On the days where there are only two or three things, write, workout, read, I go to add more add more as a habit. But would more activities lead to progress?

If your goal is to progress, you must decide which activities lead to it and which don’t. It’s hard and you’ll never be able to do it for sure but you can make a decision to. A decision to step back a decision to think about what does add to progress and cut what doesn’t.

In my latest video, I share how I got Google Cloud Professional Data Engineer Certified. I passed the exam without meeting any of the prerequisites. How? A few activities which led to progress. But the certification isn’t the real progress. The real progress comes from doing something with the skills the certificate requires. More on that in the future.







Ask a Machine Learning Engineer Anything | March 2019

Yesterday’s post was an April fools. The blog is still going and I have no intentions of stopping anytime soon.

That being said, sometimes it’s hard to think of new things. Thinking takes work. A lot has been done but there’s still far more out there we don’t know than we do know.

Writing is one of the ways to figure things out. Thoughts turn into words. Words turn into stories. Stories turn into explanations. Explanations turn into adventures.

One of the reasons I started learning machine learning was because I wrote about where I wanted to be in 3-5 years. I asked myself what was my ideal scenario. ‘If everything went my way for the next 3-5 years, where would I be?’

It’s a tough question to answer. And the answer changes slightly every now and then. I learn something new and it sways the ideal.

So what is it?

To create and learn at the intersection of health, technology and art.

I’m working towards it.

I love hearing the stories of others. How they did things. How they were faced with a decision and the reason why they did what they did.

What if I could steal some of their ideas and use them for myself?

I’ve spent the last 18-months studying machine learning online which eventually led to a job in the field.

I know there are many more people out there doing the same so every so often I host a live question and answer session on my YouTube channel. Where anyone can join and ask me anything live on the stream.

I don’t have the answer for every question (does anyone?) but for the small amount of things I do know, self-studying machine learning, learning online, maintaining health, I do my best.

And if you ever have a question I didn’t answer on stream, feel free to reach out anytime.

Keep learning.

The value of working on projects which might not work

‘Use a Molecular Biologist with programming experience to advertise for your Bioinformatics Specialization, not just a youtuber!!’

That’s the tail end of one of the comments on one of my recent YouTube videos.

It also mentioned the work I was doing wasn’t biologically or scientifically sound.

He was right. But it didn’t take the comment to make me aware. The video has a disclaimer at the start. The description has one too.

Maybe Reza didn’t see it. That’s okay, sometimes people miss disclaimers and no one ever reads the terms & conditions.

Not all projects you start are going to work.

But that doesn’t mean they’re a waste of time.

In my latest video, I use what I’ve been learning in the Coursera Bioinformatics Specialization plus the help of a genetic algorithm to mutate a DNA sequence until it changes into the right one. When it changes into the right sequence, a YouTube video loads of my best friend’s son hearing him speak for the first time.

The project works, the code runs. However, it’s not scientifically significant nor will it push the field of biology forward.

But I did learn a whole bunch about DNA, different genes, cell replication, computer science, algorithm design, hearing loss and how to research along the way. And now I know where I could improve, plus, I have a story. A story of how I built something.

When people reach out to me asking how they should learn machine learning (or anything else), I often recommend getting a foundation of knowledge and then starting to work on some projects of your own.

The next question is usually, ‘What project should I work on?’

To which my reply is usually, ‘Something which might not work.’

Why?

You’ve seen the reasons above from my personal project. But I’ve put together a few points on the benefits of working on things which might not work.

Getting comfortable with the unknown

Loss aversion is one of the main drivers of all decision making.

Losing something has six times the psychological effect as gaining something. Which means you'd have to win $600 to compensate losing $100.

This is hardwired into us. And it's a good thing. In the past, when we were hunter-gatherers and resources were scarce, losing something could mean the end.

But now, if you're reading this, you likely have more resources available to you than most people in history.

Yeah, you've heard this before. But what's the point?

Loss aversion keeps you in the known.

'I know this works so I'm going to keep doing it.'

Doing this over and over risks stasis. Followed by irrelevance. Followed by excruciating, painful decline.

All the best work comes from projects which might not work. The ones where the outcome isn't clear to begin with but instead is refined and found over time.

The next time you're avoiding the unknown, rather than think about what you're missing out on gaining, what are you afraid of losing?

The fear of loss is a far bigger driver of your decision making.

Get comfortable with the unknown.

Figuring out where you’re wrong

My bioinformatics project doesn't mean anything biologically.

The genetic algorithm I used wasn't as computationally efficient as it could be.

These are two areas I could improve on if I wanted to take it further.

Even if the project you're working on doesn't turn out to be as expected (they hardly do), at a bare minimum you'll figure out where you're wrong.

Now you know what doesn't work, you can use it as direction for what's next.

If everyone else is doing it, avoid

Projects don’t have to be what you see everywhere else.

Imagine you're in a job interview.

The other candidates have all worked on Project X.

The interviewer has heard the same story 6 times.

It's your turn. They ask you.

'What have you been working on?'

You reply.

'I've been working Project Y. It hasn't quite worked out yet but I think I know what I'm going to do next.'

'Oooo, Project Y, tell me more.'

This scenario is made up. But you get the point.

Having a project you've worked on is better than no project.

And having a project you've worked on that's different to what you easily find elsewhere is better than what everyone else has.

What can you do?

If someone has done it before, remix it with your own vibe. Combine one project with another and see what comes out.

The worst case?

You'll have a story about how you tried to mix X with Y. And it didn't work out. So you tried to add Z into the mix and then W was born. I ran out of letters.

Do the thing you've always done and you'll get the same results you've always got.

A chance to share your work

It's the story. The process. The thought process. The why behind each step.

Even if what you're working on doesn't turn out to be great. You'll still have this.

The process is as important as the outcome. The process is what will follow you to the next project.

Being able to describe your process to someone is teaching them to fish rather than giving them a fish.

The benefit of sharing your work, even if it doesn't work?

Someone else might be working on the same thing. They might want to come along and join forces.

‘Hey, I’m working on this too.’

The internet allows this kind of interaction.

Plus, people online are really quick to tell you where you're wrong.

‘Yeah, but where’s the practicality?’

If you're thinking about this, you're on the right path.

It's well and good to not be afraid of working on things which might not work.

But when does it turn into something really useful?

It's an iterative process. Ask, test, reflect, refine, repeat. There's no one answer.

It starts with making. Making something you're proud of. And then sharing it with others.

This post is an excerpt of the newsletter I send out once a month or so. If you’re interested in reading more posts like this, you can sign up for updates.

My formula for YouTube

A few people have asked me recently what are my tips for getting into YouTube.

I wanted to get into YouTube for a long time.

I made videos when I was 12 of homemade fireworks. They got taken down. 

When I was 16 I made videos of me playing RuneScape. They’re still online. 

When I was 19 my friend and I made a workout video. Our channel was going to be called From Your Bros. The video is still somewhere. 

I told my girlfriend. I want to start making YouTube videos. What of? Of what I do. Why? Because it will be fun. I was 22.

Then I started again. I started making videos I would like to watch.  And then I didn’t stop.

 

"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

An In-depth Review of Coursera’s Learning How to Learn Course

If you could have any super power, what would it be?

Flying?

Invisibility?

Super strength?

When I was younger my answer was always ‘I’d have the superpower to use any superpower.’ The equivalent of wishing for more wishes when a genie appears.

Now if you asked me again, I’d tweak my answer.

‘I’d have the superpower to learn anything.’

Similar but not as much of a cop-out.

But why bother having to learn something if you had the choice for any superpower, why not go straight to knowing everything?

Because learning is the fun part. Knowing everything already would be a boring life. Have you ever seen how a 4-year-old walks into a park? Now compare that to a 40-year-old.

If you can learn how to learn, you can apply it to any other skill. Want to learn programming? Apply your ability to learn. Want to learn Chinese? Apply your ability to learn.

To improve my ability to learn, I went through the Learning How to Learn Course on Coursera. It’s one of the most popular online courses of all time. And I can see why.

It’s taught in a simple and easy to understand manner. The concepts are applicable immediately. Even throughout the course, as you learn something in part 1, it resurfaces in part 2, 3 and 4, reinforcing the new information.

If you’ve seen the course before and are thinking about signing up, close this article and do it now, it’s worth it.

Otherwise, keep reading for a non-exhaustive list of some of my favourite takeaways.



Part 1 — What is learning?

Focused and Diffused thinking

How do you answer such a question? What is learning?

The course breaks it down with the combination of two kinds of thinking, focused and diffused.

Focused thinking involves working on a singular task. For example, reading this post. You’re devoting concentration to figuring out what the words on the screen mean.

Diffused thinking happens when you’re not focused on anything. You’ve probably experienced this on a walk through nature or when you’re laying in bed about to go to sleep. No thoughts but at the same time, all of the thoughts.

Learning happens at the crossover of these two kinds of thinking.

You do focus on something intently for a period of time and then take a break and let your thoughts diffuse.

If thinking was a flashlight, focused thinking would be a small beam of intense light and diffused thinking would be a wider beam of less intense light.

If thinking was a flashlight, focused thinking would be a small beam of intense light and diffused thinking would be a wider beam of less intense light.


How often have you been stuck on a problem and then thought of answer in the shower?

This is an example of the two kinds of thinking at work. It doesn’t always happen consciously either.

Have you ever not known what step to take next on an assignment, left it overnight, come back and saw the solution immediately?

If you want to learn something, it’s important to take advantage of the two kinds of thinking. Combine intense periods of focus with intense periods of nothing.

After studying, take a walk, have a nap or sit around and do nothing. And don’t feel bad for it. You’re giving yourself the best opportunity to let the diffused mind do its thing.



Procrastination and how to combat it

You’re working a problem. You reach a difficult point. It’s uncomfortable to keep going so you start to feel unhappy. To fix this feeling, you seek something pleasurable. You open another tab, Facebook. You see the red notifications and click them. It feels good to have someone connecting with you. You see Sarah has invited you to her birthday. You look at the invites list, Gary is there. Gary is there. You’re not the biggest fan of Gary. Back in middle school he was a prick. ‘Hey bro!’ he’d yell as he smacked you on the back. Hard. A bit more scrolling. Some advertisement for new shoes. The same ones you saw yesterday. They look real nice, black with orange. ‘I’m going to order them,’ you think.

You look down at your notes. You forget where you were up to.

What the hell just happened?

You were working on a problem. And you reached a difficult point. It was uncomfortable and you started to feel unhappy. You sought out something pleasurable. It worked. But only temporarily. Now you’re back to where you were and you’re even more upset at the fact you got distracted.

So how do you fix it?

The course suggests to use the Pomodoro technique. I can vouch for this one.

It’s simple.

You set a timer and do nothing except what you wanted to work on for the duration. Even if it gets difficult, you keep working on it until the timer is done.

A typical Pomodoro timer is 25-minutes with a 5-minute break afterwards. During the break you can do whatever you want before starting another 25-minute session.

But you can use any combination of working/break time.

For example, to complete this course, I used 1-hour Pomodoro timers with a 15-30 minute break in between.

  • 1-hour studying.

  • 15-minute break.

  • 1-hour studying.

  • 15-minute break.

  • 1-hour studying.

  • 30-minute break.

  • 1-hour studying.

If 25-minutes is too long, start with 10 and make your way up.

Why is doing this valuable?

Because you can’t control whether or not you solve every problem which arises before the end of a days work.

But you can control how much time and effort you put in.

Can control: 4-hours of focused work in a day.

Can’t control: solving every assignment question in a day.



Sleep your way to better learning

We’ve talked about focused and diffused thinking. Sleep is perhaps the best way to engage unconscious diffused thinking.

Being in focused mode is to brain cells what lifting weights is to muscles. You’re breaking them down.

Sleep provides an opportunity for them to repaired and for new connections to be formed.

Dr. Terrence Sejnowski talks about how new synapses (connections) are formed on the dendrites during sleep.

Dr. Terrence Sejnowski talks about how new synapses (connections) are formed on the dendrites during sleep.

There’s a reason famous thinkers like Einstein and Salvatore Dali would sleep for 10-hours at a time and tap multiple naps a day.

They knew it was vital for their brain to clear out toxins built up during the day which prevented engaging the focused mind.

The next time you feel like pulling an all-nighter and studying, you’d probably be better off getting a good night sleep and resuming the next day.



Spaced repetition, a little every day

Jerry Seinfeld writes jokes every day. He has a calendar on his wall and every day he writes jokes, he marks an X on it. Because he writes them every day if you looked the calendar you’d see a chain of X’s.

Once the chain has started, all he has to do is keep it going.

‘Don’t break the chain.’

This technique is not only good for writing jokes. It can be used for learning too.

In the Learning How to Learn Course, they refer to a similar concept called spaced repetition.

Spaced repetition involves practicing something in small timeframes and as you get better at it, increasing the amount of time between each timeframe.

For example, when starting to learn Chinese, you might practice a single word every day for a week until you’re good at it. Then after the first week, you practice it twice a week. Then twice a month. Then once every two months. Eventually, it will be cemented in your mind.

The best learning happens when you combine these two concepts. Don’t break the chain by practicing a little every day and incorporate spaced repetition by going over the difficult stuff more often.

To begin with, you could set yourself a goal of one Pomodoro a day on a given topic. After a week, you would’ve spent 3-hours on it. And after a year, you’ll have amassed over 150-hours. Not bad for only 25-minutes per day.

Bonus: A great tool for spaced repetition is the flash card software Anki and for not breaking the chain, I recommend the Don’t Break the Chain poster from the writers store.



Part 2 — Chunking

Part 2 of the course introduces chunking. Multiple neurons firing together are considered a chunk. And chunking is the process of calling upon these regions in a way which they work together.

Why is this helpful?

Because when multiple chunks of neurons fire together, the brain can work more efficiently.

How do you form a chunk?

A chunk is formed by first grasping an understanding of a major concept and then figuring out where to use it.

For example, if you were starting to learn programming, it would be unwise to try and learn an entire language off by heart. Instead, you might start with a single concept, let’s say loops. You don’t need to understand the language inside and out to know where to use loops. Instead, when you come across a problem which requires a loop, you can call upon the loops chunk in your brain and fill in the other pieces of the puzzle as you need.



Deliberate practice: do the hard thing

Forming a chunk is hard. First of all, what are the important concepts to learn? Second, where should you apply them?

This is exactly why you should spend time and effort trying to create them. Instead of learning every intricate detail, seek out what the major concepts are. Figure out how to apply them by testing yourself. Work through example problems.

The process of doing the hard thing is called deliberate practice. Spending more time on the things you find more difficult is how an average mind becomes a great mind.



Einstellung — don’t be held back by old thoughts

Dr. Barbara Oakley introduces Einstellung as a German word for mindset.

But the meaning is deeper than a single word translation.

Every year you upgrade your smartphone’s software. A whole set of new features arrive along with several performance improvements.

When was the last time your way of thinking had a software upgrade?

Einstellung’s deeper meaning describes an older way of thinking holding back a newer, better way of thinking.

The danger of becoming an expert in something is losing the ability to think like an amateur. You get so good at the way that’s always worked, you become blind to the new.

If you’re learning something new, especially if it’s the first time in a while, it’s important to be mindful of Einstellung. Have an open mind and don’t be afraid of asking the stupid questions. After all, the only stupid question is the one that doesn’t get asked.



Recall — what did you just learn?

Out of what you’ve read so far, what has stood out the most?

Don’t scroll back up. Put the article down and think for a second.

How would you describe it to someone else?

It doesn’t have to be perfect, do it in your own words.

Doing this is called recall. Bringing the information you’ve just learned back to your mind without looking back at it.

You can do it with any topic. Reading a book? When you’re finished, put it down and describe your favourite parts in a few sentences.

Finished an online course? Write an article about your favourite topics without going back through the course. Sound familiar?

Practicing recall is valuable because it avoids the illusion of competence. Rereading the same thing over and over again can give you an illusion of understanding it. But recalling it and reproducing the information in your own words is a way to figure out which parts you know and which parts you don’t.



Part 3 — Procrastination & Memory

The Habit Zombie

How hard do you have to think about making coffee in the morning at your house?

No very much. So little, your half asleep zombie mode body can fumble around in the kitchen with boiling water and still manage to not get burned.

How?

Because you’ve done it enough it’s become a habit. It’s the same with getting dressed or brushing your teeth. These things you can do on autopilot.

Where do habits come into to learning?

The thing about habits is that almost anything can be turned into a habit. Including procrastination.

Above we talked about combating procrastination with a timer. But how do you approach from a habit standpoint?

Part 3 of the Learning How to Learn course breaks habits into four parts.

  1. The cue — an event which triggers the next three steps. We’ll use the example of your phone going off.

  2. The routine — what happens when you’re triggered by the cue. In the phone example, you check your phone.

  3. The reward — the good feeling you get for following the routine. Checking your phone, you see the message from a friend, this feels good.

  4. The belief — the thoughts which reinforce the habit. You realise you checked your phone, now you think to yourself, ‘I’m a person who easily gets distracted.’

How could you fix this?

You only have to remove one of the four steps for the rest to crumble.

Can you figure out what it is?

The cue.

What would happen if your phone was in another room? Or turned off?

The cue would never happen, neither would the subsequent steps.

The technique of removing the cue doesn’t only work for procrastination. It can work for other habits too. It also works in reverse. If you want to create a good habit. Consider the four steps.

To make a good habit, create a cue, make a routine around it, give yourself a reward if you follow through and you’ll start forming a belief about you being the type of person who has the good habit.



The dictionary isn’t the only place product comes after process

Thinking about the outcome of your learning is the quickest way to get discouraged about it.

Why?

Because there is no end. Learning is a lifelong journey.

No one in history has ever said, ‘I’ve learned enough.’

And if they have, they were lying.

I’ve been speaking English since I was young. Even after 25-years of speech, I still make mistakes, daily. But would getting upset at where my level of English is at be helpful? No.

What could I do?

I could accept that knowing everything about speech and the English language is impossible. And instead, focus on the process of speaking.

This principle can be related to anything you’re trying to learn or create.

If you want to get better at writing, the end product could be a bestselling book. But if I told you to go and write a bestselling book, what would you write?

Worrying about what a bestselling book would have in it would consume you. It’s far more useful to focus on the process, to write something every day.



Free up your working memory and set a task list the night before

Dr. Oakley says we’ve got space for about four things in working memory. But if you’re like me, it’s probably closer to one.

Some of the people I work with have three monitors with things happening on all of them, I’m not sure how they do it. I stick to one and push it to two when a task requires it.

If I had a third monitor it would be the A5 notepad I carry around everywhere. It’s my personal assistant. Every morning, I write down a list of half a dozen or so things I want to get done during the day. Sometimes I write the same list on the whiteboard in my room to really clear out my brain.

Even when I’m in the middle of a focused session, 12 minutes into a Pomodoro, things still come out of no where. Rather than stop the Pomodoro, the thought gets trapped on the paper. Working memory free'd up.

The course recommends creating a list of things the night before but I’m fan of first thing in the morning as well. Putting things down means they’re out of your head and you can devote all of your brain to power to focused thinking rather than worrying about what it was you had to do later.

Don’t forget to add a finishing time. The time of day you’ll call it quits.

Why?

Because having a cutoff time means you’ve got a set timeframe to complete the tasks in. A set timeframe creates another reason to avoid procrastination.

And having a cutoff time for focused work means you’ll be giving your brain time to switch to diffused thinking. Who knows. That problem you couldn’t solve at 4:37 may solve itself whilst you’re in the shower at 8:13.

You’ve only got a limited number of slots in your working memory. If using a task list helps to free up one of the slots, it’s worth it.

You’ve only got a limited number of slots in your working memory. If using a task list helps to free up one of the slots, it’s worth it.

Part 4 — Renaissance learning & unlocking your potential

Learning doesn’t happen in a straight line

You could study all weekend and go back to work on Monday and no one would know.

You could work on a problem all week and by the end of the week feel like you’re worse off than you started.

Dr. Sejnowski knows this. And emphasises learning doesn’t happen linearly.

Learning tough skills doesn’t happen over the course of days or weeks or months. Years is the right timeframe for most things.

I wanted to know how quickly how quickly I could learn all of the math concepts behind machine learning: calculus, linear algebra, probability. I found a question on Quora about it.

A person who had been studying machine learning for 30-years replied with, ‘Decades.’ And then explained how he was still discovering new things.

I was impatient. I was focusing on product rather than process.

Learning looks more like a broken staircase than a straight line.

Learning looks more like a broken staircase than a straight line.

Charles Darwin was a college dropout

How do you go from medical school dropout to discovering the theory of evolution?

Easy. You be Charles Darwin.

But what if you’re not Charles Darwin?

Not to worry, not everyone is Charles Darwin.

There will never be another Charles Darwin. History repeats, but never perfectly. It’s better to think of it as a rhyme. History rhymes with the future.

Charles Darwin dropped out of medical school much to the disappointment of his family of physicians. He then travelled the world as a naturist.

Years went on but one thing never changed, he kept looking at things in nature with a child-like mind.

He’d take walks multiple times per day in between periods of study. Focused mind and diffused mind.

Take any introduction to biology course and you’ll know what happened next.

I’ve condensed a story of decades into sentences but the point is everyone has to start somewhere.

The first year you learn something new you suck. The second year you suck even more because you realise how much you don’t know.

There’s no need to envy those who seem to know what they’re doing. Every genius starts somewhere.

‘He who says he can and he who says he can’t are usually right’

The course credits Henry Ford as saying the above. But the internet is telling me it’s from Confucius.

It doesn’t matter. Whoever said it was also right.

If you believe you can’t learn something, you’re right.

If you believe you can learn something, you’re right.

Dr. Oakley was a linguist during her twenties. She didn’t like math. So how did she become an engineering professor?

By changing her belief in what she could learn.

I used to tell myself, ‘I’ll never be the best engineer.’

I said it to someone at a meetup. They replied, ‘not with that attitude.’ A simple yet profound statement. All the best ones are.

Whatever it is you decide to learn, it all starts with the story you tell yourself. Pretend you’re the hero in the story of your own learning journey. Challenges will arise. It’s inevitable. But how does the hero deal with them? You decide.

Learning: the ultimate meta-skill

Taking responsibility for your learning is one of the most important undertakings you can manage.

Learning is the ultimate meta-skill as it can be applied to any other skill. So if you want to improve your ability to do anything, learning how to learn is something you should dedicate time to.

The things I’ve mentioned in this article are only scratching the surface of what’s available in the Learning How to Learn course. I’ve left out exercise, learning with others, studying in different locations. But you can imagine the benefits these have.

If you want to dig deeper, I highly recommend checking out the full Learning How to Learn course. I paid for the certificate but it’s not needed. You can access all the materials for free. However, I find I take things more serious when I pay for them. And the information in this course is worth paying for.

“If you’re not embarrassed by who you were last year, you’re not learning enough.” - Alain de Botton

Keep learning.

PS there’s a video version of this article available on my channel. I’m a visual learner so I like to watch things and read them if I can.


Source: https://link.medium.com/nB4U2kbs3T