Deep Learning: What, Why and Applications
aiehive.com
aiehive.com
indeed. much better off reading this instead: http://karpathy.github.io/neuralnets/
First they come with
f(x,y) = xy
suddendly
df(x,y)/dx = (f(x+h,y)-f(x,y))/h
You could still use a deep learning framework as a black box without knowing how to differentiate a function. However, you'd have trouble following any relevant blog post / scientific paper, understanding how it works or developing any relevant new algorithm.
The article is well written, my brain just turns off if I read higher math.
This is a nonsensical over-generalization.
>> In addition, it is automatically do the feature extraction.
At the cost of interpretability. Let's not even mention the dreadful nights of tweaking parameters (such as dropout probability, activation function, network architecture, learning rate, optimization function, various pre-processing tricks, pre-training to warm-start, convolution parameters, maxpool parameters, and so much more).
[1] https://papers.nips.cc/paper/4443-algorithms-for-hyper-param...
ok, well, imagine (for simplicity) that you are standing on a 3d-surface with mountains and valleys and such. you can only see so far. you need to walk/run etc. to get to a pot of gold which is there at the global minima of this landscape.
what do you do ? one option is choose gradient descent :) start walking somewhere, and see if the your height, from your previous position, is decreasing. if it is, then maybe you are on the right track...
Not only that it can be done, but it works even by random trial. Sophisticated optimization techniques work out just 2x faster, so if you have cheap GPU, you can run 2x more trials and get your hyperparameters fine tuned.
If you want to apply optimization, you can use one of the popular libraries like hyperopt and MOE.
Deep learning is not "better" than other forms of algorithmic prediction. There's a best tool for every job
Anyway, as a non-expert I've always thought that inferential statistical methods only work adequately when correct assumptions about the underlying distribution, hence also about the underlying analytical model and/or causal relationships are made, and I wonder how deep learning approaches deal with that issue.
How do humans deal with the same question? Context. Understanding. Background knowledge. The go-to rebuke of the smug liberal cognoscenti: 'educate yourself'.
To be able to interact appropriately with humans, AI needs to understand not only its own subject matter but the preconceptions and prejudices of the humans with whom it interacts.
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