I have recently started to use NLTK toolkit for creating few solutions using Python.
I hear a lot of community activity regarding using stanford NLP. Can anyone tell me what is... moreI have recently started to use NLTK toolkit for creating few solutions using Python.
I hear a lot of community activity regarding using stanford NLP. Can anyone tell me what is the difference between NLTK and Stanford NLP ? Are they 2 different libraries ? i know that NLTK has an interface to stanford NLP but can anyone throw some light on few basic differences or even more in detail.
Can stanford NLP be used using Python?
I'm conceptualizing a solver for a variant of sudoku called multi-sudoku, where multiple boards overlap like so:
If I understand the game correctly, you must solve each grid... moreI'm conceptualizing a solver for a variant of sudoku called multi-sudoku, where multiple boards overlap like so:
If I understand the game correctly, you must solve each grid in such a way that the overlap between any two or more grids is part of the solution for each.
I'm unsure as to how I should be thinking about this. Anybody got any hints/conceptual clues? Additionally, if any topics in artificial intelligence come to mind, I'd like to hear those too.
Out of curiosity, I've been reading up a bit on the field of Machine Learning, and I'm surprised at the amount of computation and mathematics involved. One book I'm reading... moreOut of curiosity, I've been reading up a bit on the field of Machine Learning, and I'm surprised at the amount of computation and mathematics involved. One book I'm reading through uses advanced concepts such as Ring Theory and PDEs (note: the only thing I know about PDEs is that they use that funny looking character). This strikes me as odd considering that mathematics itself is a hard thing to "learn."
Are there any branches of Machine Learning that use different approaches?
I would think that a approaches relying more on logic, memory, construction of unfounded assumptions, and over-generalizations would be a better way to go, since that seems more like the way animals think. Animals don't (explicitly) calculate probabilities and statistics; at least as far as I know. less
i have some data and Y variable is a factor - Good or Bad. I am building a Support vector machine using 'train' method from 'caret' package. Using 'train' function i was able to... morei have some data and Y variable is a factor - Good or Bad. I am building a Support vector machine using 'train' method from 'caret' package. Using 'train' function i was able to finalize values of various tuning parameters and got the final Support vector machine . For the test data i can predict the 'class'. But when i try to predict probabilities for test data, i get below error (for example my model tells me that 1st data point in test data has y='good', but i want to know what is the probability of getting 'good' ...generally in case of support vector machine, model will calculate probability of prediction..if Y variable has 2 outcomes then model will predict probability of each outcome. The outcome which has the maximum probability is considered as the final solution)
**Warning message: In probFunction(method, modelFit, ppUnk) : kernlab class probability calculations failed; returning NAs**
sample code as below
library(caret) trainset <- data.frame( class=factor(c("Good", "Bad", "Good",... less
I want to know what a learning curve in machine learning is. What is the standard way of plotting it? I mean what should be the x and y axis of my plot?
How can I set Neural Networks so they accept and output a continuous range of values instead of a discrete ones? From what I recall from doing a Neural Network class a couple of... moreHow can I set Neural Networks so they accept and output a continuous range of values instead of a discrete ones? From what I recall from doing a Neural Network class a couple of years ago, the activation function would be a sigmoid, which yields a value between 0 and 1. If I want my neural network to yield a real valued scalar, what should I do? I thought maybe if I wanted a value between 0 and 10 I could just multiply the value by 10? What if I have negative values? Is this what people usually do or is there any other way? What about the input?
Thanks less
When working with image or text-based data, are there any neural network architectures that can handle input with different sizes? If not, what are the best ways to handle these... moreWhen working with image or text-based data, are there any neural network architectures that can handle input with different sizes? If not, what are the best ways to handle these kinds of data?
I am trying learn deep learning and specifically using convolutional neural networks. I'd like to apply a simple network on some audio data. Now, as far as I understand CNNs are... moreI am trying learn deep learning and specifically using convolutional neural networks. I'd like to apply a simple network on some audio data. Now, as far as I understand CNNs are often used for image and object recognition, and therefore when using audio people often use the spectrogram (specifically mel-spectrogram) instead of the signal in the time-domain. My question is, is it better to use an image (i.e. RGB or greyscale values) of the spectrogram as the input to the network, or should I use the 2d magnitude values of the spectrogram directly? Does it even make a difference?
Thank you. less
Lastly, I started to learn neural networks and I would like know the difference between Convolutional Deep Belief Networks and Convolutional Networks. In here, there is a similar... moreLastly, I started to learn neural networks and I would like know the difference between Convolutional Deep Belief Networks and Convolutional Networks. In here, there is a similar question but there is no exact answer for it. We know that Convolutional Deep Belief Networks are CNNs + DBNs. So, I am going to do an object recognition. I want to know which one is much better than other or their complexity. I searched but I couldn't find anything maybe doing something wrong.
I am learning assembly language in my spare time to become a better developer.
I understand the difference between stack-based machines and register-based machines at a conceptual... moreI am learning assembly language in my spare time to become a better developer.
I understand the difference between stack-based machines and register-based machines at a conceptual level, but I am wondering how stack-based machines are actually implemented. If a virtual machine, e.g. JVM or .NET, runs on a register-based architecture, e.g. x86 or x64, then it must use registers at the assembly level (as far as I am concerned). I am obviously missing something here. Therefore I am unsure of the distinction at assembly language.
I have read articles on here e.g. Stack-based machine depends on a register-based machine? and also on Wikipedia, but I don't believe they answer my question directly. less
I am new to the field of neural networks and I would like to know the difference between Deep Belief Networks and Convolutional Networks. Also, is there a Deep Convolutional... moreI am new to the field of neural networks and I would like to know the difference between Deep Belief Networks and Convolutional Networks. Also, is there a Deep Convolutional Network which is the combination of Deep Belief and Convolutional Neural Nets?
This is what I have gathered till now. Please correct me if I am wrong.
For an image classification problem, Deep Belief networks have many layers, each of which is trained using a greedy layer-wise strategy. For example, if my image size is 50 x 50, and I want a Deep Network with 4 layers namely
My input layer will have 50 x 50 = 2500 neurons, HL1 = 1000 neurons (say) , HL2 = 100 neurons (say) and output layer = 10 neurons, in order to train the weights (W1) between Input Layer and HL1, I use an AutoEncoder (2500 - 1000 - 2500) and learn W1 of size 2500 x 1000 (This is unsupervised learning). Then I feed forward all images through the first hidden layers to obtain a set of... less