
Kaggle Learn review: there is a deep learning track and it is worth your time - akashtndn
https://techandmortals.wordpress.com/2018/01/27/kaggle-learn-review-there-is-a-deep-learning-track-and-it-is-worth-your-time/
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minimaxir
Since the "How Do I Learn AI/ML" question pops up on Hacker News once a month
(most recent:
[https://news.ycombinator.com/item?id=16138353](https://news.ycombinator.com/item?id=16138353)),
here's my comments:

Yes, Al/ML MOOCs teach the corresponding tools well, and the creation of new
tools like Keras make the field much more accessable. The obsolete gatekeeping
by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years
research experience" is one of the things I _really_ hate about the industry.

 _However_ , contrary to the thought pieces that tend to pop up, taking and
passing a MOOC doesn't mean you'll be an expert in the field (and this applies
for most MOOCs, honestly). They're very good for learning an overview of the
technology, but _nothing_ beats appling the tools on a real-world, _noisy_
dataset, and solving the inevitable little problems that crop up during the
process.

Reviewing the Keras documentation ([https://keras.io](https://keras.io)) and
examples ([https://github.com/keras-
team/keras/tree/master/examples](https://github.com/keras-
team/keras/tree/master/examples)) are honestly much better teachers of AI/ML
than any MOOC, in my opinion.

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nharada
While I agree that PhD gate-keeping is frustrating, I've found a sizable
subset of the people who say that really mean "we want you to have the
mathematical foundation for this", not "we require a PhD". I don't have a PhD,
but I've found that generally, as long as I can show I have the theory down,
employers don't seem to mind.

~~~
minimaxir
The PhD reference in the comment is more toward other comments made on Hacker
News/Reddit/Medium thought pieces.

For _job hunting_ , the credibility issue is more nuanced, and I wrote a
separate rant about that a couple weeks ago:
[https://twitter.com/minimaxir/status/951117788835278848](https://twitter.com/minimaxir/status/951117788835278848)

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dbecker
I'm the lead on the Kaggle Learn project, and the author of the deep learning
track.

I'm happy to answer questions here.

I agree with the commenter saying you need to do your own projects to
understand these topics.

Our deep learning track is meant to be the fastest path to knowing enough to
do your own projects. You can do the entire track, including the hands-on
exercises, in a single sitting.

We won't make you an expert in an afternoon, but you'll know enough to start
doing your own projects. For most people that's also the point where Deep
Learning becomes fun enough that you'll find time to keep learning.

Kaggle Learn is still in a very early stage. We'll add more lessons soon. But
we'll stay committed to the goal of getting you up-to-speed quickly, so you
can take on your own projects.

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ultrasounder
A very nice playbook style breakdown of steps to ease in to Machine Learning
and eventually Deeplearning Of should I call it Differential Programming???
Keep it up and hope to see more of your writing. May I also add the very
approachable book by Tariq Rashid
[https://github.com/makeyourownneuralnetwork](https://github.com/makeyourownneuralnetwork).

Ananth

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akashtndn
Owing to my inexperience with the topic, I am not sure if the term
'differential programming' belongs here. However, this book looks promising.
Thanks.

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sandGorgon
@akash - good article. Love to see some more in-depth stuff next time !

~~~
akashtndn
Thanks and definitely.

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bootcat
Thank you ! I was thinking to put time into deep learning and it seems like
this is a good way to start !

