
How to learn Deep Learning in 6 months - antonp
https://towardsdatascience.com/how-to-learn-deep-learning-in-6-months-e45e40ef7d48
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YeGoblynQueenne
Here's Peter Norvig arguing that you shouldn't:

 _Teach Yourself Programming in Ten Years_

[https://www.norvig.com/21-days.html](https://www.norvig.com/21-days.html)

The article is about programming, specifically about books etc advising you on
how to learn programming in ten days etc, but it totally applies to Deep
Learnign also.

Deep Learning is an enormous discipline. In the current point in the hype
cycle, there are literally thousands of papers published every month.
Personally, I doubt anyone, evern the luminaries of the field are keeping up
with it. 6 months is about enough to dip your toes into the preliminaries, and
that's if you're willing to skip the relative background [1].

Of course, that's all true if you want to _learn_. If you just want to train
yourself in the use of a few tools, then 6 months is plenty. But don't expect
to be able to solve any new problems that way- someone else will always have
to do all the hard work for you.

________

[1] A bit of background will help with questions such as: Why was Deep
Learning necessary in the first place? (hint: search for "constant error
carousel"); did Deep Learning just spring fully-formed from the brow of
Hinton/ Schmidhuber? (hint: read wikipedia on "machine learning" and
"Artificial Intelligence"); etc. If you're not asking those questions then
you're holding it wrong.

~~~
warabe
I totally agree with you.

I started Deep Learning Specialization in Coursera:
[https://www.coursera.org/specializations/deep-
learning](https://www.coursera.org/specializations/deep-learning) last month
and almost finished it, but I realized this field requires a lot of expertise,
not something you can learn in a month. What I learned in the courses was just
a basic topics in Deep Learning and how to use Numpy, TensorFlow and Keras.

I was considering diving into a Data Science job and started that
specialization as a starting point, but I just realized how foolish I am.
Chances are I'll find a job, but it definitely takes another 10 years/10,000
hours to master this discipline.

Anyway, the specialization is wonderful and Dr.Ng explains complicated Deep
learning topics in a way that is understandable for everyone. So if just
learning is what you want, you should take it, but I don't think you are
prepared for a real world Data Science job after finishing it.

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bryanrasmussen
How to make background to a picture that is geeky for geeky subjects in 10
minutes:

1\. find javascript file that has been compressed on your system. 2\. open in
color coded editor 3\. take screenshot

there you go, perfect 'code' background for your deep learning article!

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ReverseCold
I'm trying to do something similar by starting the other way around (first why
it works, then how it works) and so far it's working for me pretty well.[1]

For reference, I'm in high school, so I don't really have the necessary math
background to understand everything first try-but I think I'll be able to push
through and understand it.

The top down (fast.ai) approach didn't really work for me - as I felt like I
didn't really understand how/why everything works. Learning the math behind it
forces you to understand.

[1] [https://nikhiljha.com/2018/02/06/Neural-Networks-First-
Steps...](https://nikhiljha.com/2018/02/06/Neural-Networks-First-Steps/)

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mruniverse
Sounds like a project manager. Step 1) Prepare for project. 1-2 weeks. Step 2)
Do programming 3-4 weeks. Step 3) Unit tests and QA 2 weeks.

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pixelperfect
My impression is that a lot of programmers have already followed a similar
path, would like to find a job doing ML, but haven't found a way to make it
happen. Does that sound correct? I'm not saying the effort isn't worth it
anyway.

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gt_
How to learn in 3 months:

Complete said material in 3 months.

~~~
thisisit
With double the effort too ie instead of 10-20 budget for 20-40 hours each
week.

~~~
gt_
For some that might be triple the effort. I guess it depends on _a lot of
things_.

