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Thanks for commenting here, Adam. I was wondering if you could address the issues that sz4kerto brought up. Do you think that deep learning is better than machi

Thanks for commenting here, Adam. I was wondering if you could address the issues that sz4kerto brought up. Do you think that deep learning is better than machine learning? And do you define deep learning as a neural network with >= 3 layers as opposed to machine learning being < 3 layers? I don't see those claims anywhere on your website and I have no idea where the grandparent comment got those statements from. Frankly, they don't make any sense. But I do share his or her sentiment that the phrase "deep learning" is overused, poorly understood and is rapidly becoming a meaningless buzzword akin to "Big Data" or "Web 2.0". I don't expect you to explain statements that you don't appear to have made, but I would like to hear an expert's view on exactly what deep learning is and how it compares to other machine learning techniques. I understand if you've already addressed this issue in your book and we can read your thoughts on this when it's published. But just a few sentences here might do a lot to clear up some misconceptions for fellow hn readers.

agibsonccc 12y ago
I think there's trade offs. I apologize if our marketing copy seems excessive, I'd love to fix that and take feedback seriously. Please do email me. Deep Learning done right applied to unstructured data is a great part of either an ensemble of methods or great for working with hard to engineer features. I think as for normal machine learning where you are typically doing feature engineering, you need to understand what's going on to make recommendations for actionable insights. Everything has its place. I'd like to echo Andrew Ng here, the hype in deep learning is overblown. While there are great results, it's not magic. Neural networks still need feature scaling, among other things to work well. Much of the hype comes from the giant marketing machine that are the PR firms for the research labs who need new data scientists. While great work is being done in these labs, much of it isn't going to be applicable in the day to day work of a data scientist just trying to do some some basic A/B testing. Hope that makes sense!
sz4kerto 12y ago
http://www.skymind.io/contact.html http://www.skymind.io/contact.html Quote:" On a technical level, deep-learning networks are distinguished from the more commonplace single-hidden-layer neural networks by their depth; that is, the number of node layers through which data is passed in a multistep process of pattern recognition. More than three layers, including input and output, is deep learning. Anything less is machine learning. The number of layers affects the complexity of the features that can be identified." Again, it's great what you are doing in general, I just brought this up as a example of the buzzwords and hype around deep learning.
- agibsonccc 12y ago
  I see. We can modify that ;). Thanks for the shout out. Criticisms are how we learn.
- blueyes 12y ago
  this is fixed jfyi

agibsonccc 12y ago

I think there's trade offs. I apologize if our marketing copy seems excessive, I'd love to fix that and take feedback seriously. Please do email me. Deep Learning done right applied to unstructured data is a great part of either an ensemble of methods or great for working with hard to engineer features. I think as for normal machine learning where you are typically doing feature engineering, you need to understand what's going on to make recommendations for actionable insights. Everything has its place. I'd like to echo Andrew Ng here, the hype in deep learning is overblown. While there are great results, it's not magic. Neural networks still need feature scaling, among other things to work well. Much of the hype comes from the giant marketing machine that are the PR firms for the research labs who need new data scientists. While great work is being done in these labs, much of it isn't going to be applicable in the day to day work of a data scientist just trying to do some some basic A/B testing. Hope that makes sense!

sz4kerto 12y ago

http://www.skymind.io/contact.html http://www.skymind.io/contact.html Quote:" On a technical level, deep-learning networks are distinguished from the more commonplace single-hidden-layer neural networks by their depth; that is, the number of node layers through which data is passed in a multistep process of pattern recognition. More than three layers, including input and output, is deep learning. Anything less is machine learning. The number of layers affects the complexity of the features that can be identified." Again, it's great what you are doing in general, I just brought this up as a example of the buzzwords and hype around deep learning.

agibsonccc 12y ago
I see. We can modify that ;). Thanks for the shout out. Criticisms are how we learn.
blueyes 12y ago
this is fixed jfyi