‘Hi, I’m a machine learning engineer from Google.’
‘Hello everyone, I’m a software engineering at Intuit.’
‘Good morning, my name is Sandy, I’m a freelance data scientist.’
I was scared to post my introduction.
What was I going to say?
‘Hi, I’m Daniel, I quit the first programming course I signed up to, so now I’m here to try again.’
I probably could’ve said that. That’s actually not too bad. At least it was the truth.
It was a week before the Udacity Deep Learning Nanodegree began. Everyone was introducing themselves in the Slack channel.
I was the odd one out. It seemed everyone had already some experience with machine learning or data science and here I was writing my first line of Python 3-weeks prior.
The course went on. I graduated 4-months later.
Then I went on and did Udacity’s AI Nanodegree as part of my online AI Masters Degree.
And Andrew Ng’s deeplearning.ai course on Coursera.
Then I got hired as a machine learning engineer.
All in the space of a year.
But 2 months? Woah.
That’s some ambition. I love it.
Let’s be clear though, the field of deep learning and all the other encompassing crafts is huge. You’ve seen that photo of the iceberg right? The one where most of it is under water and only the top 10% of it sticks out of the water. Yeah, that’s like deep learning.
The art of building deep learning neural networks can be picked up in a couple of months by someone with a little programming experience.
So how would you do it?
Done some Python work before? Go ahead with these steps. If not, learn some Python first and come back.
These two books will help you go from a deep learning rookie to a deep learning practitioner.
‘But Daniel, these books cost money?’
You’re right. But the education is worth it. Especially since $100 worth of books, if used correctly, could give you provide you the skills for a $100,000+ role. Or better still, provide you with the skills to build something incredible.
If you can’t afford these books, I’d recommend Siraj Raval’s or Sentdex’s YouTube channel.
‘I’ve got a little longer than two months.’
Putting together a deep learning model is often one of the last pieces of the puzzle.
Even the best deep learning models won’t help anyone if there’s no data for them to learn on.
Deep learning is the combination of statistics, programming, math and engineering. Maybe a few more things.
If you’ve got longer than two months, I’d still recommend the two books above. As well as any of the courses I’ve mentioned.
But the key thing is to try out what you’ve learned.
Find a problem which interests you and see if you can apply deep learning to it.
‘Yeah, people always say that, find something that interests you, but I’m not interested in anything…’
Not interested in anything?
Then what pisses you off?
Maybe you’re pissed off at the amount of homeless in your city. How can you use deep learning to find more people a home?
Or perhaps you don’t like the state of the healthcare system, does deep learning have a place in building a healthier world?
A major part of being a good deep learning engineer is to seek out new ways of looking at and solving existing problems.
How long per day?
I heard an interview once with a world chess champion.
‘How much do you play per day?’
‘Never more than 3-hours per day.’
‘Yeah, but during those 3-hours, all I’m doing is playing chess.’
3-hours per day? That’s it?
I had to try it. So I did.
I set a timer. One hour, 5-minute break, another hour, 15-minute break, then another hour.
It took me all three hours to get through one function.
The next day I finished the whole assignment in the same timeframe.
I still do this kind of study every day.
Sometimes you may be able to do more. Other days less. But the main thing is to focus on one thing at a time.
Especially if you want to learn deep learning in two months. Block all the other crap out and learn deep learning. I have to remind myself of this all the time.
Never half ass two things, whole ass one thing. You’ll be surprised what you’re capable of.
You might even end up running down your street after finishing your final coding project.