How I'm Learning Deep Learning - Part IV

Is a wealth of data the final frontier?


This article is part of the How I’m Learning Deep Learning Series on Medium:

Part I: A new beginning.
Part II: Learning Python on the fly.
Part III: Too much breadth, not enough depth.
Part IV: AI(ntuition) versus AI(ntelligence). (You’re currently reading this)
Extra: My Self-Created AI Master’s Degree

A few of the resources helping me break into the world of AI. — Hinton Image  Source

A few of the resources helping me break into the world of AI. — Hinton Image Source

A lot has happened since Part III. While the last couple of articles went in-depth into what exactly I was learning, this one will be a little different. Rather than break it down week by week, I’ll cover the major milestones.

I graduated from the Udacity Deep Learning Nanodegree (DLND) in August last year. Thinking about how I emailed the support team asking what the refund policy was before starting the course makes me laugh. It was easily one of the best learning programs I’ve ever taken. If you’re after more details, I recently published an in-depth review video on the DLND.

Making videos about my journey has led to some great conversations with others on the same path. I met someone in Canada who was doing almost the exact same courses as me. Even more interesting is that we have the same poster of Arnold in our room. Small world.

More recently, I had the opportunity to have a conversation with Shaik Asad, a 14-year-old AI developer from India. He teaches himself AI after completing his homework. Since then we’ve been actively chatting about life and our other interests. Seeing how passionate Shaik is about AI and hearing what his goals are, inspired is an understatement.

AI Master’s Degree

After graduating from the DLND, I was a deer in the headlights. I’d learned all about the amazing power of deep learning (DL) but still didn’t fully understand what really made deep neural nets tick. I was also left wondering whether DL is the be all and end all of Artificial Intelligence (more on this later).

I needed to know more. Curiosity led me to create my own AI Master’s Degree. Having a rough outline of a curriculum to follow allowed me to narrow down how I would spend my time. My mission is to use AI to help people move more and eat better. I have skin in the game in the world of fitness and nutrition, I’m working on the AI side.

In the past few months, I’ve completed 80% of the Coursera Deep Learning specialisation (course 5 was just released as of writing) by Andrew Ng and the team at as well as Term 1 of the Udacity Artificial Intelligence Nanodegree (AIND).

For those who learn from a ground-up approach, the specialisation is the best place to start learning about DL. If you’re more into diving into project building, or want to progress with one of Udacity’s advanced Nanodegrees, start with the DLND.

Term 1 of the AIND covered classical AI approaches. It lost me at times due to my lack of programming ability and my recent focus on DL. However, learning about how far the field has come since inception was fascinating.

I’m into Term 2 now, which includes building projects in computer vision, natural language processing and speech recognition using DL. Back in a familiar fishbowl.

I’ll release a full in-depth review of the and AIND once I’ve completed them both.

Future of AI

After learning more about how DL works, I started to become suspicious of what its longevity prospects are. Many DL models need a ridiculous amount of data to produce a useful output.

This is well and good if you’re one of the two companies in the world with enough data to keep Titanic afloat but not so good if you’re a young AI hopeful. Deep Learning has brought about many incredible insights but many of which are in the realm of supervised learning, which still takes a lot of human input.

Although our ability to gather and produce data is increasing exponentially, I’m not convinced more data is the key to solving all of our AI problems.

Are we really just data-processing machines? Last century, people thought our internal processes could be modelled using the concept of steam engines. The man with a hammer problem comes to mind.

Back-propagation (an algorithm to help neural networks improve themselves) does not work very well on unlabelled data, which is what most of the universe is comprised of.

Consider a four-year-old walking into a room they’ve never been in. The young child doesn’t require 10,000 labelled images of a room to know how to navigate it. They don’t even require one labelled image of a room, they simply start interacting with it.

Even the godfather of Deep Learning seems to be thinking along the same lines. In an interview late last year, Geoffrey Hinton was asked his opinion on the current state of AI techniques.

“My view is throw it all away and start again.” 
 “The future depends on some graduate student who is deeply suspicious of everything I have said.”

Listening to the lectures and talks of Monica Anderson (especially the one on dual-process theory) and discovering her work on Artificial Intuition as an approach to Artificial Intelligence raises more questions on the matter.

I will delve deeper into these topics in a future post.

Next Steps

Over the next few months, I will be completing the curriculum I have set out for myself.

I just submitted the first major project of Term 2 of the AIND, a computer vision model to detect facial keypoints.

For each of the upcoming major projects, I will be posting an article detailing my understanding of the work as well as a step-by-step guide for those looking to build an equivalent.

I’m also thoroughly enjoying the free courses on offer from MIT on Artificial General Intelligence and Deep-Learning for Self-Driving Cars.

After completing the AIND, seems likely to be my next port of call.

By the time I finish up my curriculum, I will be looking to move to the US to join a startup in the world of health and AI (or create my own). If you know anyone or think I should be following anyone currently playing at the crossroads of health and AI, please let me know.

For those considering embarking on their own self-led learning journey or finding out more about AI, the words of Naval Ravikant sum it up perfectly.

The current education system is a path depended outcome. We have the internet now, if you actually have a desire to learn, everything is on the internet. The ability, means and tools to learn are abundant and infinite, it’s the desire to learn that’s incredibly scarce.

See you in Part V.


The One Thing All Humans Want

This photo has the most likes on my Instagram.


YES! I feel great. I made it over 100.

Look at my numbers going up on YouTube. I’m kind of a big deal.


1000’s of people are watching my videos, this is incredible! It’s hard not to be ecstatic about those pretty little green arrows.

Screen Shot 2018-01-13 at 7.14.46 pm.jpg

29 people LOVE my Tweet! 7 people even shared it with their followers! Even more people get to see what I have to say!

Did I forget to tell you about the million answer views I just crossed on Quora? That felt incredible. I finally made it.

How about the claps on my latest Medium story? The claps mean the most to me.


All the likes, retweets, applause, sure it feels great. Little hits of dopamine here there and everywhere. It’s like a playing a slot machine with infinite spins. But they’re not what keep me going.

The numbers are fun to watch, sometimes painful to watch, but mostly fun. They were designed to be like that. To keep us coming back.

Why else would I wake up every morning eager to check my email or see how many views my Quora answers got overnight? All of these systems play on one vital piece of human psychology.

The need to feel important.

The numbers don’t drive me. It’s what’s behind the numbers that drives me.

Everyone who read my article, who watched my video, who listened to my podcast. Even If I’ve never met them, I feel like I have. I know this because that’s how I feel when I watch someone else's video or read their writing.

When I know there’s someone on the other end of my creations, I feel important. I feel like a superhero.

After I’ve hit publish on a piece of work, I can do anything. I could climb a building or punch an asteroid away from Earth.

I’ve put a bit of my soul or whatever it is inside me out to the universe.


There isn’t one person I’ve ever met who doesn’t like feeling important.

Do you want to know something beautiful? When you make someone else feel important, it often rubs off on you. DOUBLE WIN!

If you can make yourself feel important and not rely so much on external influences such as likes, subscriber counts and views, you win. I’m still trying to get better at this.

I’m selfish. Most of my works start off being for myself. Sitting here alone with my thoughts typing out answers like these or making YouTube videos makes me feel important. Putting words on a page or seeing myself talk on a screen helps me decipher what my role is in the Netflix special of my life.


Have you ever stopped to think about how much more likely the store clerk is to help you out if you stopped to ask them about their day?

I asked Ashlee how her day was going. She tried to speak but she couldn’t. It was like she had forgotten how to talk. She had been so used to taking coffee orders and then moving onto the next customer. She apologised, “I’m not used to other people asking me how my day is.” You can imagine the service I received after that simple gesture. DOUBLE WIN!


I didn’t know what Ashlee’s name was when I asked her how her day was. After small talk, I asked her name.

I had to remind myself three times what it was. “All the tricks, Ashlee Smashlee, Ashlee in a Tree, Ashlee likes Coffee” I recited over and over. I still forgot her name.

When we finished talking, I quickly took out my second brain and typed her name into my names note. I have a note with names of people I’ve met and could potentially meet in the future.

Names are a very simple way to make someone feel important. How many times do you think Ashlee hears, “Hey, how’s it going?” versus “Hey Ashlee, how’s your day?” The small things are the big things.

Have you ever been walking through a crowd of people with dozens of conversations going on at once and yet you’re still able to hear someone in distance calling your name clear as day?

“A person’s name is the sweetest sound they can ever hear.” - Dale Carnegie

Make someone feel important, remember their name.


I’m sweating now. It’s so hot where I live, the rain made it worse. I didn’t even notice until a bead of sweat hit the desk in front of me.

I’m lucky. I’ve found what makes me feel important. Creating, writing, helping others. It makes me feel so important it puts me in a trance and I end up sitting in a pile of my own sweat.

What makes you feel important? What makes you end up sitting in a pile of your own sweat like me?

Even better…

How can you make others feel important? DOUBLE WIN!

I need a shower.