Date of publication: 2017-07-08 19:02
The second important quantity to track while training a classifier is the validation/training accuracy. This plot can give you valuable insights into the amount of overfitting in your model:
Extreme learning machines (ELM) are basically FFNNs but with random connections. They look very similar to LSMs and ESNs, but they are not recurrent nor spiking. They also do not use backpropagation. Instead, they start with random weights and train the weights in a single step according to the least-squares fit (lowest error across all functions). This results in a much less expressive network but it 8767 s also much faster than backpropagation.
The comparative genomics approach compares two or more genomes (the total heritable portion of an organism). Traditional visual presentations have centered on linear tracks with connecting lines to show points of similarity or difference. In this project you will overlay large amounts of comparative data on a set of 8D surfaces which are controlled and interfaced by using human interaction, like the Xbox Kinect.
Lots of sites I use need several links to display/access very simple information. So, I seem to spend ages linking around hyperspace to see information which would easily fit on one page. Could I build a tool which would allow a user to define a new, single page, that had all the data concerned?
Hi, thanks for the very nice visualization! A common mistake with RNNs is to not connect neurons within the same layer. While each LSTM neuron has its own hidden state, its output feeds back to all neurons in the current layer. The mistake also appears here.
“When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.”
Also, is there some specific name for the ordinary autoencoder to let people know that you are talking about an autoencoder that compresses the data? Perhaps 8775 compressive autoencoder 8776 ? To me, the term 8775 autoencoder 8776 includes all kinds of autoencoders, . also denoising, variational and sparse autoencoders, not just 8775 compressive 8776 (?) autoencoders. (So to me it feels a bit wrong to talk about 8775 autoencoders 8776 like all of them compress the data.)
Use relative error for the comparison. What are the details of comparing the numerical gradient \(f’_n\) and analytic gradient \(f’_a\)? That is, how do we know if the two are not compatible? You might be temped to keep track of the difference \(\mid f’_a - f’_n \mid \) or its square and define the gradient check as failed if that difference is above a threshold. However, this is problematic. For example, consider the case where their difference is 6e-9. This seems like a very appropriate difference if the two gradients are about , so we’d consider the two gradients to match. But if the gradients were both on order of 6e-5 or lower, then we’d consider 6e-9 to be a huge difference and likely a failure. Hence, it is always more appropriate to consider the relative error :
Great article! I am currently working on my thesis and this very similar to what I am writing but only a bit better. Thank you for the clear summary of a somewhat complex theory about time series predictions!
Could you please enhance the article by adding and presenting the Hidden Markov models using exactly the same approach ? That would be very enlightening, and I am very curious to read your explanation. Thank you
OK, how much time do you waste learning to use a new software package? And, how many computer systems that you know of invisible, or nearly invisible in the sense that they assist you BUT, don't intrude on your non-computer work patterns? Simple examples that you may be familiar with are ABS, Traction Control, automobile engine management systems. But, what other ones can you think of? Of course, this sounds like ubiquitous computing, however, we are going beyond this. Our goal is the production of systems which can be installed in a work environment, either computerised or not, and have almost zero learning effort, but, which will make life easier.
Check only few dimensions. In practice the gradients can have sizes of million parameters. In these cases it is only practical to check some of the dimensions of the gradient and assume that the others are correct. Be careful : One issue to be careful with is to make sure to gradient check a few dimensions for every separate parameter. In some applications, people combine the parameters into a single large parameter vector for convenience. In these cases, for example, the biases could only take up a tiny number of parameters from the whole vector, so it is important to not sample at random but to take this into account and check that all parameters receive the correct gradients.