10 ms·

Google's Free Deep Learning Course

614 points by olivercameron 11y ago

wodenokoto 11y ago
Does this use tensor flow?
- isoos 11y ago
  From the site: "Complete learning systems in TensorFlow will be introduced via projects and assignments."
  - th0br0 11y ago
    Purely video-based though, no materials at all, no transcript. That's a no-go for me.
    - lqdc13 11y ago
      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.
      - nbouscal 11y ago
        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.
  - wodenokoto 11y ago
    I must have gone blind, thank you :)
- ganeshkrishnan 11y ago
  Yes it does. I wonder if it's overkill but Google has to push tensorflow to the market some how
  - pramodliv1 11y ago
    Why do they have to push the library. Are there any competitive advantages?
    - Estragon 11y ago
      So they can spend less time onboarding new engineers.
ganeshkrishnan 11y ago
I think intro to machine learning https://www.udacity.com/courses/ud120 https://www.udacity.com/courses/ud120 is the prerequisite to this course
- jokoon 11y ago
  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.
  - rahimnathwani 11y ago
    You might like the book 'data science from scratch' by Joel Grus. He uses Python without ML libraries and explains clearly what's going on.
    - j_s 11y ago
      http://amzn.com/B00W4DTP2A http://amzn.com/B00W4DTP2A
  - wrsh07 11y ago
    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."
    - pconner 11y ago
      NN&DL is great for neural nets specifically, it doesn't really cover other branches of ML. That being said, I found it very easy to understand with no prior ML knowledge, all you need is some calculus and linear algebra experience.
stared 11y ago
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.)
stared 11y ago
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.
it_learnses 11y ago
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?
- drcode 11y ago
  It sort of makes you wonder if we're looking at a future where something like 5 teams composed of 1000 of the top researchers each are going to build the premier ML systems, and those teams can then solve most generalized ML tasks. Most other programmers wouldn't be able to contribute much value in a world that worked like this. This perhaps mirrors how the chip market works, which similarly involves a limited number of researchers involved in advanced manufacturing techniques that are highly specialized and mostly a mystery to other people in the technology field.
  - conventionalmem 11y ago
    But there may be a larger market to hire people who know how to use the tools they design.
    - drcode 11y ago
      The caveat is that in the long term, ML systems are generalized systems that function independently and won't necessarily always remain in the form of an "API tool" that traditional programmers will interface with.
- stared 11y ago
  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.
magicmu 11y ago
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!!
- mrborgen 11y ago
  Haven taken this course (though I will), but check out Andrew Ng's Coursera's ML course. He gives a crash course in linear algebra. It helped me a lot in my endeavours in ML & neural nets. I also like to learn as I go along, and this intro was just about enough to get started.
  - pramodliv1 11y ago
    I did not find the crash course in Linear Algebra to be useful. I had to complete Strang's course (ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/) before doing Andrew Ng's course , after which most of the course seemed easy.
  - aswanson 11y ago
    Ng is an awesome lecturer, very easy to follow.
- jskonhovd 11y ago
  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_4_pages.pdf http://minireference.com/static/tutorials/linear_algebra_in_... If you are motivated, you will do fine. Good luck!
DrNuke 11y ago
Yes! Andrew Ng's coursera + kaggle.com + this deep learning course by Google is a very nice -and free- foundation.
fiatjaf 11y ago
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).
j2kun 11y ago
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?
- tibbe 11y ago
  Because he wrote one of the most successful ones?
  - j2kun 11y ago
    My goal is to understand it for my own purposes, not to put on blinders and replicate his work.
  - GigabyteCoin 11y ago
    "Just trust me, I'm smarter than you are." isn't the best stance for a teacher to take.
- stared 11y ago
  It's a course assuming some background in ML. For _probability_ vectors, there is a distance measure called cross-entropy. It's a standard error measure in classification problems. It has some important properties different from the Euclidean distance (especially for low probabilities) and there is information-theoretic interpretation. More on it: - http://stats.stackexchange.com/questions/80967/qualitively-what-is-cross-entropy http://stats.stackexchange.com/questions/80967/qualitively-w...
  - j2kun 11y ago
jorgecurio 11y ago
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.
imh 11y ago
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/machinelearning/ https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearni...
- cbgb 11y ago
  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.
alok-g 11y ago
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.
maurits 11y ago
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/
ntnlabs 11y ago
Yeap, it's dead :)
cpal90 11y ago
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.
- cowsandmilk 11y ago
  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 11y ago
    Great thanks for the reply!

614 points by olivercameron 11y ago

wodenokoto 11y ago

Does this use tensor flow?

isoos 11y ago
From the site: "Complete learning systems in TensorFlow will be introduced via projects and assignments."
- th0br0 11y ago
  Purely video-based though, no materials at all, no transcript. That's a no-go for me.
  - lqdc13 11y ago
    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.
    - nbouscal 11y ago
      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.
- wodenokoto 11y ago
  I must have gone blind, thank you :)
ganeshkrishnan 11y ago
Yes it does. I wonder if it's overkill but Google has to push tensorflow to the market some how
- pramodliv1 11y ago
  Why do they have to push the library. Are there any competitive advantages?
  - Estragon 11y ago
    So they can spend less time onboarding new engineers.

ganeshkrishnan 11y ago

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

jokoon 11y ago
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.
- rahimnathwani 11y ago
  You might like the book 'data science from scratch' by Joel Grus. He uses Python without ML libraries and explains clearly what's going on.
  - j_s 11y ago
    http://amzn.com/B00W4DTP2A http://amzn.com/B00W4DTP2A
- wrsh07 11y ago
  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."
  - pconner 11y ago
    NN&DL is great for neural nets specifically, it doesn't really cover other branches of ML. That being said, I found it very easy to understand with no prior ML knowledge, all you need is some calculus and linear algebra experience.

stared 11y ago

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.)

stared 11y ago

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.

it_learnses 11y ago

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?

drcode 11y ago
It sort of makes you wonder if we're looking at a future where something like 5 teams composed of 1000 of the top researchers each are going to build the premier ML systems, and those teams can then solve most generalized ML tasks. Most other programmers wouldn't be able to contribute much value in a world that worked like this. This perhaps mirrors how the chip market works, which similarly involves a limited number of researchers involved in advanced manufacturing techniques that are highly specialized and mostly a mystery to other people in the technology field.
- conventionalmem 11y ago
  But there may be a larger market to hire people who know how to use the tools they design.
  - drcode 11y ago
    The caveat is that in the long term, ML systems are generalized systems that function independently and won't necessarily always remain in the form of an "API tool" that traditional programmers will interface with.
stared 11y ago
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.

magicmu 11y ago

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!!

mrborgen 11y ago
Haven taken this course (though I will), but check out Andrew Ng's Coursera's ML course. He gives a crash course in linear algebra. It helped me a lot in my endeavours in ML & neural nets. I also like to learn as I go along, and this intro was just about enough to get started.
- pramodliv1 11y ago
  I did not find the crash course in Linear Algebra to be useful. I had to complete Strang's course (ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/) before doing Andrew Ng's course , after which most of the course seemed easy.
- aswanson 11y ago
  Ng is an awesome lecturer, very easy to follow.
jskonhovd 11y ago
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_4_pages.pdf http://minireference.com/static/tutorials/linear_algebra_in_... If you are motivated, you will do fine. Good luck!

DrNuke 11y ago

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

fiatjaf 11y ago

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).

j2kun 11y ago

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?

tibbe 11y ago
Because he wrote one of the most successful ones?
- j2kun 11y ago
  My goal is to understand it for my own purposes, not to put on blinders and replicate his work.
- GigabyteCoin 11y ago
  "Just trust me, I'm smarter than you are." isn't the best stance for a teacher to take.
stared 11y ago
It's a course assuming some background in ML. For _probability_ vectors, there is a distance measure called cross-entropy. It's a standard error measure in classification problems. It has some important properties different from the Euclidean distance (especially for low probabilities) and there is information-theoretic interpretation. More on it: - http://stats.stackexchange.com/questions/80967/qualitively-what-is-cross-entropy http://stats.stackexchange.com/questions/80967/qualitively-w...
- j2kun 11y ago

jorgecurio 11y ago

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.

imh 11y ago

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/machinelearning/ https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearni...

cbgb 11y ago
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.

alok-g 11y ago

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.

maurits 11y ago

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/

ntnlabs 11y ago

Yeap, it's dead :)

cpal90 11y ago

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.

cowsandmilk 11y ago
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 11y ago
  Great thanks for the reply!