I'm doing a language recognizer, I had planned to classify my i-vectors with neural networks, but I've read a lot of papers and they always use other methods like SVM or PLDA, can someone explain to me why? or it's fine to do it with neural networks?
Neural networks are good for complex non-linear multifeature input. I-vectors by design map speaker space to very simple space where speakers are easily separated with logistic regression or SVM.
If you want to try with neural networks, try something end-to-end like https://github.com/FlashTek/vggvox-pytorch
Related
Basically, I'm looking for any way to use Pytorch neural networks to classify sequences of numbers. After doing some searching, it seems like LSTM layers are the way to go, but all the examples I've found online involve classification of sequences of text, not numbers. I'm wondering if there are any examples of neural networks that can classify sequences of numbers.
I am interested in machine translation and more specific I would like to examine the similarity between two strings. I would like to know if there are deep learning methods for text feature extraction. I already tried the famous statistics methods like cosine similarity, Levenstein distance, word frequency and others.
Thank you
To find the similarity between 2 string ,try to train a Siamese networks
on your dataset
Siamese networks are a special type of neural network architecture. Instead of a model learning to classify its inputs, the neural networks learns to differentiate between two inputs. It learns the similarity between them.
https://medium.com/#gautam.karmakar/manhattan-lstm-model-for-text-similarity-2351f80d72f1
The below is the link of a kaggle competition ,they have used siamese networks for text simmilarity
https://medium.com/mlreview/implementing-malstm-on-kaggles-quora-question-pairs-competition-8b31b0b16a07
Hope this clears your doubts
I am trying to code an OCR for shop tickets (in Java), I have good results with image dictionary distance, but not for skewed texts or bad scans.
I heard that neuronal networks are perfect for this.
Question: which type of neuronal network do you recommand for shop tickets character detection ?
Thks
Neural networks will not magically solve the problem for you. They will have similar problems that your current approach has. Most likely you will have to detect skew and correct it before sending it to a classifier.
Similarly with bad scans. It depends what exactly a bad scan is. For example, some neural networks are amazingly efficient at correcting blurs (unfocused image, blur by move, ...).
Have a look at some papers about OCR and neural networks. It is a classical topic so there are many. For example The Anatomy of Bangla OCR System for Printed Texts Using Back Propagation Neural Network also tries to solve the problem of skewed images before running a neural network.
I know that recurrent neural networks can be used for OCR. Even a very simple one will easily recognize simple characters. There is a recent paper that improves upon them: High-Performance OCR forPrinted English and Fraktur using LSTM Networks. They even include text-line normalization which may be very useful in your case.
Notice that there is an answer here about training a normal Feed-forward backpropagation neural network for OCR too: training feedforward neural network for OCR
"Convolutional Neural Networks" with "Deep Learning" have been shown to give some of the best results in OCR (specifically on the MNIST database).
A good starting point is this tutorial.
I have a different sets of vectors for an object. These vectors are different and are extracted from a particular shape. I want to train my Neural Network in matlab to recognize this particular shape. So that when I input another different vectors of similarity of that particular object, the neural network is able to differentiate and output either '1' or '0'
I am new to this neural network stuffs and I hope that someone could give me some valuable pointers.
First of all have a look to this pdf explaining the Neural Network Toolbox.
Here you can download a tutorial on pattern recognition with neural networks with matlab.
I hope this helps on your task.
To understand machine learning concepts in general and neural networks in particular, this resource will be usefull www.ml-class.org
I am a student working on an internship project where in we are using Bayesian networks to predict a possible outcome from a given set of discrete parent variables.We now intend to use artificial neural network to do the task.So could any one please help me out with the similarities and differences between Bayesian networks and artificial neural network?Any suggestions as how to proceed with migration would be helpful.
Thanks
Similarity
Both use directed graphs.
Difference
In Bayesian networks the vertices and edges have meaning- The network structure itself gives you valuable information about conditional dependence between the variables. With Neural Networks the network structure does not tell you anything.