
Google's Free Deep Learning Course - olivercameron
https://udacity.com/course/deep-learning--ud730
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j2kun
I'm going through the course right now, and the instructor is saying some
strange things, clearly (to me) ignoring that what he's saying is only true in
very specific contexts.

For example, in the video I just watched he said "the natural way to compute
the distance between two vectors is using cross entropy." And then he goes on
to describe some unnatural features of cross entropy. The truly "natural" way
to compute distances between vectors is the Euclidean distance, or at least
any measure that has the properties of a metric.

I can understand this is a crash course and there isn't time to cover nuances,
but I'd much rather the instructor say things like "one common/popular way to
do X is..." rather than making blanket and misleading statements. Or else how
can I trust his claims about deep learning?

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tibbe
Because he wrote one of the most successful ones?

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j2kun
My goal is to understand it for my own purposes, not to put on blinders and
replicate his work.

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imh
If you want more than a 4 lecture course, I recommend Nando de Freitas's
course. It's very high quality and free.

[https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearni...](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)

~~~
cbgb
To be clear, the posted course is not a survey course in machine learning. It
is instead a more practical course on using TensorFlow to build deep neural
network architectures useful for certain tasks.

The link the OP posted is a (great) survey course dedicated to machine
learning as a whole, which includes methods other than deep learning.

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stared
When it comes to the course itself (I've just started it) it looks nice, but
the (initial) questions tend to be vague.

E.g. in the first question with code I had to reverse-engineer what they mean
(including passing values in a format, which I consider non-standard
(transpose!)). The first open-ended questions were entirely "ahh, you meant
this aspect of the question".

Otherwise, the course (the general level, pace, overview) seems nice.

EDIT:

The IPython Notebook tasks (i.e. the core exercises) are nice.

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ganeshkrishnan
I think intro to machine learning
[https://www.udacity.com/courses/ud120](https://www.udacity.com/courses/ud120)
is the prerequisite to this course

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jokoon
I was just beginning to give it a try, it just requires you to type the code
that is shown on video. Poor way of teaching something, it seems at first. I
sense this course is just to teach me the tools of the trade, not really
enabling students to fully understand what they're doing.

On the other hand, some months ago I watched the ML course by Andrew Ng, and I
still did not understand how to test a simple linear regression for myself, so
I did not really understand it, and stopped watching the course.

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wrsh07
From the yc reading list 2015
[[http://themacro.com/articles/2015/12/yc-2015-reading-
list/](http://themacro.com/articles/2015/12/yc-2015-reading-list/)], they
recommend [for Neural Networks] this book:
[http://neuralnetworksanddeeplearning.com/](http://neuralnetworksanddeeplearning.com/)

It's more about understanding than "learning tools."

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datawrangler
I really liked the NN&DL course. Another I've been meaning to check out is
this book: [http://www.deeplearningbook.org](http://www.deeplearningbook.org)

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maurits
For people interested, Stanford has an excellent online course on deep-
learning with an emphasis on convolutional networks. [1]

It comes with video, notes, all the math, cool ipython notebooks and will let
you implement a deepish network from scratch. That includes doing backprop
through the svn, softmax, max-pool, conv and ReLU layers.

After that you should be more than capable to build a 'real' net using your
favourite lib (Tensorflow, theano etc).

[1]: [http://cs231n.stanford.edu/](http://cs231n.stanford.edu/)

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stared
While TensorFlow may be not yet as mature as Theano or Torch, I love their
tutorial:
[https://www.tensorflow.org/versions/master/tutorials/](https://www.tensorflow.org/versions/master/tutorials/).
It's clean, concrete, and more general than introduction to their API. (Before
I couldn't find anything comparable in Theano or Torch.)

In any case, I regret waiting so long for learning deep learning. (I thought
that I needed to have many years of CUDA/C++ knowledge (I have none); but in
fact, what I need to to know the chain rule, convolutions etc - things I've
learnt long time ago.)

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DrNuke
Yes! Andrew Ng's coursera + kaggle.com + this deep learning course by Google
is a very nice -and free- foundation.

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magicmu
How accessible is a course like this with no prior knowledge of linear
algebra? I know it's listed in the pre-reqs, but with a good head for math and
lots of calc, is it something that could be picked up along the way? I'm
normally pretty bold about stuff like that, but I know it's a core part of
deep learning / ML. If it's really necessary, if anyone has any resources for
linear algebra run-throughs it would be greatly appreciated!!

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jskonhovd
I would start with the nano degree program if you don't have any prior
knowledge of ML.

Udacity has a Linear Algebra review course, but I don't believe it is public
for now. I had taken a linear algebra course before I took the GT ML class,
but I wasn't a expert by any means. I don't believe you will need a deep
understanding of linear algebra before taking this class. Singular value
decomposition might come up. I think if you are familiar with everything in
the following pdf you should be fine.

[http://minireference.com/static/tutorials/linear_algebra_in_...](http://minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf)

If you are motivated, you will do fine. Good luck!

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datawrangler
Udacity has a couple free linear algebra courses.

One here: [https://www.udacity.com/course/linear-algebra-refresher-
cour...](https://www.udacity.com/course/linear-algebra-refresher-course--
ud953)

And another here: [https://www.udacity.com/course/intro-algebra-review--
ma004](https://www.udacity.com/course/intro-algebra-review--ma004)

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cpal90
Hi guys one question sorry if it's answered somewhere but why does the title
say "Free" course? is it free cause of the trial period or the whole course is
free as of now?

If the whole course is free are there more free courses on this site?

Thanks for the reply.

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cowsandmilk
I believe all the courses on udacity are free. They make money through
offering nanodegrees, partnering with companies, and a partnership with
Georgia Tech to offer an online master's degree in computer science.

So free to take, money if you want degrees.

~~~
cpal90
Great thanks for the reply!

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alok-g
Will the projects/assignments be workable on Windows, or would I need Linux et
al for these?

And if not natively (using docker/VMs), would they be able to use NVidia CUDA
card on my system? And how much disk space would be needed.

Thanks.

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wodenokoto
Does this use tensor flow?

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isoos
From the site: "Complete learning systems in TensorFlow will be introduced via
projects and assignments."

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th0br0
Purely video-based though, no materials at all, no transcript. That's a no-go
for me.

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lqdc13
Videos are also really short. If you don't already know how neural networks
work, you won't learn it here.

This, I think, is more of a library demonstration than anything.

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nbouscal
It does start really basic, with a single logistic classifier. I think you'd
have to be pretty motivated to learn how neural networks work from this
course, but it seems possible. If you don't know any machine learning at all,
then you probably wouldn't be able to.

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ntnlabs
Yeap, it's dead :)

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it_learnses
Would it be beneficial for me as a developer to take these machine learning
courses? I took a course in the uni a while back and know the general
techniques, but I'm not sure how it would help me in my career unless I'm
doing some cutting edge work in the field or focusing on a machine learning
career, in which case wouldn't I need to be pursuing a postdoc or something in
it?

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stared
Likely. Data analysis (of which ML is an important part) is needed in many
places, from entry-level to top-level.

I am a data science freelancer and I mostly do projects for IT-dominated
companies. First, I was surprised that such companies need some external help
with relatively simple tasks; only later I discovered that top-notch
performance in webdev (or even: algorithms) does not mean that someone is able
to do simplest data analysis.

For data science / ML - I know a lot of openings in which they are looking for
"data scientists", but what they mean is software engineers with at least a
slight idea what is data analysis.

When it comes to deep learning in particular - I don't know.

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zo1
>" _For data science / ML - I know a lot of openings in which they are looking
for "data scientists", but what they mean is software engineers with at least
a slight idea what is data analysis._"

I've been wanting to get into this field recently. Do you have more info about
these openings, perhaps?

~~~
stared
Now I don't track offers (I get contracts through recommendations/networking),
so I may be not up-to date. My background is different (PhD in quantum
physics), so for me stats/data/ML is simple, but software architecture,
algorithms - not as much.

When 3 years ago I was looking for data science internships, most of interview
were strictly in software engineering. (I got into a more data-analysis
oriented.) Even when I applied to Google a year ago (and failed), all non-
trivial questions where in software engineering (some with data-oriented
paradigms, tough).

Look at [https://medium.com/@rchang/my-two-year-journey-as-a-data-
sci...](https://medium.com/@rchang/my-two-year-journey-as-a-data-scientist-at-
twitter-f0c13298aee6#.kx5dz2iud) \- the taxonomy of "Type A Data Scientist" vs
"Type B Data Scientist" is useful. You want to apply for the "B" or even -
software engineer in a company which deals with data and is open to shifting
roles.

Going back to the interviews: I see that the set of questions is entirely
different. E.g. if the first question is "how to invert a binary table" or
"how to test if a black-box number generator is fair". But sometimes it is not
clear from the job opening.

EDIT:

If you are interested in my background: [http://p.migdal.pl/2015/12/14/sci-to-
data-sci.html](http://p.migdal.pl/2015/12/14/sci-to-data-sci.html)

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jorgecurio
I fucking love Google, it's the greatest company there is. Thank you for this
free course, incredibly high quality and very enjoyable to watch.

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fiatjaf
Udacity is kinda ridiculous, making us answer some stupid questions every 5
minutes. I'm not in school anymore (by the way: no one ever learns anything in
school).

