Is it possible to train a neural network without backpropagation?
Many neural network books and tutorials spend a lot of time on the backpropagation algorithm, which is essentially a tool to compute the gradient. Let's assume we are building a model with ~10K parameters / weights. Is it possible to run the optimization using some gradient free optimization algorithms? I think computing the numerical gradient would be too slow, but how about other methods such as
Importance of local response normalization in CNN
I've found that Imagenet and other large CNN makes use of local response normalization layers. However, I cannot find that much information about them. How important are they and when should they be used? From http://caffe.berkeleyvision.org/tutorial/layers.html#data-layers: "The local response normalization layer performs a kind of “lateral inhibition” by normalizing over local input regions. In
Neural networks vs support vector machines: are the second definitely superior?
Many authors of papers I read affirm SVMs is superior technique to face their regression/classification problem, aware that they couldn't get similar results through NNs. Often the comparison states that SVMs, instead of NNs, Have a strong founding theory Reach the global optimum due to quadratic programming Have no issue for choosing a proper number of parameters Are less prone to overfitting Nee
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How to plot 20 years of daily data in time series
