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Guys, please stop posting this kind of old stuff. Or at least other guys - please stop upvoting this. It's a very old tutorial, there are tons of better ones,

Guys, please stop posting this kind of old stuff. Or at least other guys - please stop upvoting this. It's a very old tutorial, there are tons of better ones, in every aspect, today. Somebody once smartly observed that the ratio of upvotes / comments is a good indicator if something is really good. If the ratio is very high it's a red flag. This article is a perfect example - nobody has anything interesting to say about it... Maybe HN admins could take this into account maybe into the sorting algo.

xchip 10y ago
What are those better tutorials? I'm interested.
- option_greek 10y ago
  I'm currently going through this which is to the point and easily understandable: http://neuralnetworksanddeeplearning.com/ http://neuralnetworksanddeeplearning.com/ My next step is https://www.tensorflow.org/tutorials/ https://www.tensorflow.org/tutorials/ as I want to move to TF with basics already in grasp.
- TheAlchemist 10y ago
  I really liked those ones: - CS321 from Standford (outstanding material by Andrej Karpathy): http://cs231n.github.io/ http://cs231n.github.io/ - Hugo Larochelle videos: https://www.youtube.com/channel/UCiDouKcxRmAdc5OeZdiRwAg https://www.youtube.com/channel/UCiDouKcxRmAdc5OeZdiRwAg - Michael Nielsen tutorial: http://neuralnetworksanddeeplearning.com/ http://neuralnetworksanddeeplearning.com/ - Chris Olah blog: http://colah.github.io/ http://colah.github.io/ - Keras blog: https://blog.keras.io/ https://blog.keras.io/ There is also this one (not really a tutorial though): - Goodfellow, Bengio, Courville: http://www.deeplearningbook.org/ http://www.deeplearningbook.org/ Also, there were a lot of 'Ask HN' topics - take a look.
- Dolores12 10y ago
  the one that was really easy to grasp and gave me good intuition about the subject is https://karpathy.github.io/neuralnets/ https://karpathy.github.io/neuralnets/
thearn4 10y ago
On a side note, do does it feel to anyone else like this area is a bit of a moving target? Granted this isn't uncommon for topics experiencing a lot of growth, but it's a bit troubling to me that there seems to be a lack of reference publications that experts in the area can all agree on as a starting point of reference. I honestly don't feel like blogs or framework tutorials are a great replacement for this. Also, I don't know of any other topic area where I would look at a resource that describes fundamental building blocks in an instructive way in 2014 and say "don't read this, it's irrelevant 2-3 years later". For languages/libraries/frameworks, sure. But for basic theory? That strikes me as very alien.
- posterboy 10y ago
  I agree, that makes it very opaque, but it seems that moocs and espially that one on coursera are continuously popular
- romaniv 10y ago
  >Also, I don't know of any other topic area where I would look at a resource that describes fundamental building blocks in an instructive way in 2014 and say "don't read this, it's irrelevant 2-3 years later". For languages/libraries/frameworks, sure. But for basic theory? Yeah. A lot of deep learning papers boil down to "we tried to use X architecture on Y dataset, and it seems to produce small error rate". I don't know of any other area of computer science where getting results from an algorithm without explaining how those results occur is publishable.
posterboy 10y ago
>Maybe HN admins could take this into account maybe into the sorting algo. It is, via exponential decay proprtional (among other) to that upvote/comment ratio. Can't find the source atm. So I take that as an opportunity to comment without voting as long as I can't downvote, but also as caution of needles comments when I can only upvote so much.
stablemap 10y ago
Unfortunately it seems to me that most machine learning articles fit this criterion.

xchip 10y ago

What are those better tutorials? I'm interested.

option_greek 10y ago
I'm currently going through this which is to the point and easily understandable: http://neuralnetworksanddeeplearning.com/ http://neuralnetworksanddeeplearning.com/ My next step is https://www.tensorflow.org/tutorials/ https://www.tensorflow.org/tutorials/ as I want to move to TF with basics already in grasp.
TheAlchemist 10y ago
I really liked those ones: - CS321 from Standford (outstanding material by Andrej Karpathy): http://cs231n.github.io/ http://cs231n.github.io/ - Hugo Larochelle videos: https://www.youtube.com/channel/UCiDouKcxRmAdc5OeZdiRwAg https://www.youtube.com/channel/UCiDouKcxRmAdc5OeZdiRwAg - Michael Nielsen tutorial: http://neuralnetworksanddeeplearning.com/ http://neuralnetworksanddeeplearning.com/ - Chris Olah blog: http://colah.github.io/ http://colah.github.io/ - Keras blog: https://blog.keras.io/ https://blog.keras.io/ There is also this one (not really a tutorial though): - Goodfellow, Bengio, Courville: http://www.deeplearningbook.org/ http://www.deeplearningbook.org/ Also, there were a lot of 'Ask HN' topics - take a look.
Dolores12 10y ago
the one that was really easy to grasp and gave me good intuition about the subject is https://karpathy.github.io/neuralnets/ https://karpathy.github.io/neuralnets/

thearn4 10y ago

On a side note, do does it feel to anyone else like this area is a bit of a moving target? Granted this isn't uncommon for topics experiencing a lot of growth, but it's a bit troubling to me that there seems to be a lack of reference publications that experts in the area can all agree on as a starting point of reference. I honestly don't feel like blogs or framework tutorials are a great replacement for this. Also, I don't know of any other topic area where I would look at a resource that describes fundamental building blocks in an instructive way in 2014 and say "don't read this, it's irrelevant 2-3 years later". For languages/libraries/frameworks, sure. But for basic theory? That strikes me as very alien.

posterboy 10y ago
I agree, that makes it very opaque, but it seems that moocs and espially that one on coursera are continuously popular
romaniv 10y ago
>Also, I don't know of any other topic area where I would look at a resource that describes fundamental building blocks in an instructive way in 2014 and say "don't read this, it's irrelevant 2-3 years later". For languages/libraries/frameworks, sure. But for basic theory? Yeah. A lot of deep learning papers boil down to "we tried to use X architecture on Y dataset, and it seems to produce small error rate". I don't know of any other area of computer science where getting results from an algorithm without explaining how those results occur is publishable.

posterboy 10y ago

>Maybe HN admins could take this into account maybe into the sorting algo. It is, via exponential decay proprtional (among other) to that upvote/comment ratio. Can't find the source atm. So I take that as an opportunity to comment without voting as long as I can't downvote, but also as caution of needles comments when I can only upvote so much.

stablemap 10y ago

Unfortunately it seems to me that most machine learning articles fit this criterion.