I am currently working on autoencoders using R. I have tried with the autoencoder package in R, but I am unable to interpret the results. So I am trying to write a R code for autoencoders. My question is how encoding and decoding is done in autoencoders. Is there any formula or algorithm for it? Can anyone please help me with it?
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I have been using tensorflow quantum(TFQ) for quantum machine learning rencently but there still some problems. I wanna use amplitude encoding to encode classical data like MNIST images into quantum circuit but dont know how to do it. TFQ seems not have functions to do the amplitude encoding directly?
Wish someone can give me some advise about amplitude encoding implementation with TFQ. Thank you so much.
I am doing my project based on health care.I am going to train my autoencoders with the symptoms and the diseases i.e my input is in textual form. Will that work? (I am using Rstudio).Please anyone help me with this
You have to convert the text to vectors/numbers. To do this traditional approaches like Bag of words, Tf-Idf will help but the latest Neural Word Embedding like Word2Vec, RNN Language model etc are the best techniques to obtain numeric representation of text.
Please use any Neural Word Embedding technique and convert the text(word level[word2vec], document level[doc2vec]) into numbers/vectors.
Now these vectors come with some dimension and to compress this representation to even smaller dimension u can use AutoEncoder.
Feel Free to ask any other information required.
Try using Python for these tasks as it has the latest packages.
You can use Autoencoder on Textual data as explained here.
Autoencoder usually worked better on image data but recent approaches changed the autoencoder in a way it is also good on the text data.
have a look at this.
the code is also available in GitHub.
I am a newbie. I think idea of NuPIC is really cool and therefore wanted to apply KNN Classifier on
NuPIC's output. I saw there is a KNNClassifier object already in python. I am confused about the input
patter that I should use. In case of MNIST dataset I will be having images where each image is a 2D
array of numbers and will be sparse. I can understand the format of output can be encoded using
categorical encoder in NuPIC but there is no such example of encoding an input that comes in the
form of arrays.
Any help will be highly appreciated.
This might help: http://numenta.org/search.html?q=mnist. There are some good discussions on our mailing lists about MNIST.
Now, I would like to practice some problem solving by use RCGA (Real Code Genetic Algorithms) in MATLAB. Could any helper provide me some example project matlab files about that ?
Thank you very much.
I wrote an article a few years ago on an application of genetic algorithms in MATLAB, which comes with example data and MATLAB code. Although the example doesn't use real-coding, it does demonstrate how to apply a custom chromosomal coding, so perhaps it would be a starting point for you that you could adapt.
i wanted to know that is there any algorithm that can be used for efficient feature detection from an image, i am currently using SIFT algorithm in MATLAB , but the results aren't good! So if someone could please suggest me a better algo that can make my work easier in MATLAB .
i suggest for using SURF algorithm, it's better and faster, and i think it has been implemented in MATLAB