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 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 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.’


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.

The four things that matter most | January 2019

I send out a newsletter once a month of what I’ve been up to. This is the issue for January.

Hey everybody,

Happiest of New Years.

I read a tweet the other night.

‘Lifelong 2nd mom passed today at 5:40 pm. 2 yrs ago she was teaching aerobics % working full-time @ a local investment firm, went to a Dr. appt and learned cancer was in every major organ overnight. Hug your people.’

It hit me hard. How quickly things can change.

At the end of each year, my family and I go to an island not far from my home city. There’s not much to do on the island except sit around and be with each other and think.

Think about how the past year has gone and what the next one will hold.

2018 was incredible. If you asked 2017 me if half the things in 2018 would happen, I would’ve been hesitant.

I’ve got some ambitious goals for 2019. Some very specific, some general. But they each fit into a theme.

‘Subtract and 10x.’

Subtract the things that don’t matter and 10x the things that do.

Things that matter (to me):


I’m nothing without the people around me or without a healthy relationship with myself.


If you don’t have health, what else do you have? 

And as you read in the story above, this can change on a dime. So you might as well do your best to take care of it.


I was at the gym the other night with Dave.

‘Do you know how crazy I would be if I didn’t have things to work  on?’ I said.

‘I’m the same mate.’

My mission is to create to learn and learn to create. Making art for the sake of learning something and learning something for the sake of making art. There’s probably a beautiful Japanese word for this but I don’t know it.

Man needs a mission. Whatever it is. Having a mission gives you meaning. A spouse, a child, a business, an art project, improving your health, all of the above. It doesn’t matter. All valid.


Glass almost made it into my top 3 movies. Maybe it did. It’ll take another watch before I decide. If you haven’t seen it, you should see it. I cried during the ending.

I left the cinema in the stratosphere. I sent voice memos to 3 of my friends frothing about how much I loved it.

Why? Because nothing energises me more than a powerful story.

Experiences lead to stories.

That’s it. Everything else is up for debate. Everything else is getting subtracted.

The default is often more. More more more. But too many options leads to no options. If you’re confused on what to do next, try thinking less. Not more. Less but better.

Here’s a snapshot of some of my work over the past month.


- 4000 Subscriber Q&A [

‘How do I start learning data science?’
‘How do I get fit?’
‘How do you get an internship?’
‘How do you stay motivated when working on something?’

These are the kind of questions I answered in a recent live stream to celebrate 4000 YouTube subscribers. Thank you all for checking out my work.


- Six health hacks for getting and staying lean [article]*

1. There is no best diet (anything other than the standard)
2. Snacking is a myth
3. Be prepared (learn to cook)
4. Sleep (quantity and quality)
5. Build movement into your day
6. Break the rules, move on and get back to it

These are the six principles I use to stay lean and shredded year round.

I’m not interested in 8-week challenges. Or revolutionary supplements. Health is a long game. 80+ years not 8-weeks.

*Video version of the article coming to the channel later this week.


- A Gentle Introduction to Exploratory Data Analysis [article]

Most of the machine learning model building I did during my AI Masters Degree was on prepared datasets. All I had to do was design the model and call the fit function.

After getting a role as a machine learning engineer, I found out data in the real world is pretty different.

I had to level up my exploratory data analysis skills fast. 

I'm still learning. But one of the ways I learn things best is if I write about it. So that's what I did.

I put together an article on how to take a dataset and explore it, explaining each of the steps along the way.

Read it here:

 - My YouTube Trailer for 2019 [video]

My brother is getting into making videos. He wanted to practice editing by making me a YouTube trailer.

And he did an epic job. Keep up the effort Sam.

It's got everything you can expect from my channel in 2019.

- Non-monetary forms of happiness [article]

These were fun to think about. And a good reminder.

Hug your people,


PS feel free to reach out anytime. You can reply to this email or message me on Telegram.

If you’d like to receive this direct to your inbox every month, you can sign up here.