First up, this is most certainly homework (so no full code samples please). That said...
I need to test an unsupervised algorithm next to a supervised algorithm, using the Neural... moreFirst up, this is most certainly homework (so no full code samples please). That said...
I need to test an unsupervised algorithm next to a supervised algorithm, using the Neural Network toolbox in Matlab. The data set is the UCI Artificial Characters Database. The problem is, I've had a good tutorial on supervised algorithms, and been left to sink on unsupervised.
So I know how to create a self organising map using selforgmap, and then I train it using train(net, trainingSet). I don't understand what to do next. I know that it's clustered the data I gave it into (hopefully) 10 clusters (one for each letter).
Two questions then:
How can I then label the clusters (given that I have a comparison pattern)?
Am I trying to turn this into a supervised learning problem when I do this?
How can I create a confusion matrix on (another) testing set to compare to the supervised algorithm?
I think I'm missing something conceptual or jargon-based here - all my searches come up with supervised learning... less
First up, this is most certainly homework (so no full code samples please). That said...
I need to test an unsupervised algorithm next to a supervised algorithm, using the Neural... moreFirst up, this is most certainly homework (so no full code samples please). That said...
I need to test an unsupervised algorithm next to a supervised algorithm, using the Neural Network toolbox in Matlab. The data set is the UCI Artificial Characters Database. The problem is, I've had a good tutorial on supervised algorithms, and been left to sink on unsupervised.
So I know how to create a self organising map using selforgmap, and then I train it using train(net, trainingSet). I don't understand what to do next. I know that it's clustered the data I gave it into (hopefully) 10 clusters (one for each letter).
Two questions then:
How can I then label the clusters (given that I have a comparison pattern)?
Am I trying to turn this into a supervised learning problem when I do this?
How can I create a confusion matrix on (another) testing set to compare to the supervised algorithm?
I think I'm missing something conceptual or jargon-based here - all my searches come up with supervised learning... less
I have read the answer here. But, I can't apply it on one of my example so I probably still don't get it.
Here is my example: Suppose that my program is trying to learn PCA... moreI have read the answer here. But, I can't apply it on one of my example so I probably still don't get it.
Here is my example: Suppose that my program is trying to learn PCA (principal component analysis). Or diagonalization process. I have a matrix, and the answer is it's diagonalization:
A = PDP-1
If I understand correctly:
In supervised learning I will have all tries with it's errors
My question is:
What will I have in unsupervised learning?
Will I have error for each trial as I go along in trials and not all errors in advance? Or is it something else? less
I'm having trouble with some of the concepts in machine learning through neural networks. One of them is backpropagation. In the weight updating equation,
delta_w = a*(t -... moreI'm having trouble with some of the concepts in machine learning through neural networks. One of them is backpropagation. In the weight updating equation,
delta_w = a*(t - y)*g'(h)*x
t is the "target output", which would be your class label, or something, in the case of supervised learning. But what would the "target output" be for unsupervised learning?Can someone kindly provide an example of how you'd use BP in unsupervised learning, specifically for clustering of classification?Thanks in advance.
First up, this is most certainly homework (so no full code samples please). That said...
I need to test an unsupervised algorithm next to a supervised algorithm, using the Neural... moreFirst up, this is most certainly homework (so no full code samples please). That said...
I need to test an unsupervised algorithm next to a supervised algorithm, using the Neural Network toolbox in Matlab. The data set is the UCI Artificial Characters Database. The problem is, I've had a good tutorial on supervised algorithms, and been left to sink on unsupervised.
So I know how to create a self organising map using selforgmap, and then I train it using train(net, trainingSet). I don't understand what to do next. I know that it's clustered the data I gave it into (hopefully) 10 clusters (one for each letter).
Two questions then:
How can I then label the clusters (given that I have a comparison pattern)?
Am I trying to turn this into a supervised learning problem when I do this?
How can I create a confusion matrix on (another) testing set to compare to the supervised algorithm?
I think I'm missing something conceptual or jargon-based here - all my searches come up with supervised learning... less
Well, basically i want to know what does the fit() function does in general, but especially in the pieces of code down there.
Im taking the Machine Learning A-Z Course because im... moreWell, basically i want to know what does the fit() function does in general, but especially in the pieces of code down there.
Im taking the Machine Learning A-Z Course because im pretty new to Machine Learning (i just started). I know some basic conceptual terms, but not the technical part.
CODE1:
from sklearn.impute import SimpleImputer
Some other example where I still have the doubt
CODE 2:
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
print(sc_X)
X_train = sc_X.fit_transform(X_train)
print(X_train)
X_test = sc_X.transform(X_test)
I think that if I know like the general use for this function and what exactly does in general, I'll be good to go. But certaily I'd like to know what is doing on that code
I have a TensorFlow model that I built (a 1D CNN) that I would now like to implement into .NET.In order to do so I need to know the Input and Output nodes.When I uploaded the... moreI have a TensorFlow model that I built (a 1D CNN) that I would now like to implement into .NET.In order to do so I need to know the Input and Output nodes.When I uploaded the model on Netron I get a different graph depending on my save method and the only one that looks correct comes from an h5 upload. Here is the model.summary():
If I save the model as an h5 model.save("Mn_pb_model.h5") and load that into the Netron to graph it, everything looks correct:
However, ML.NET will not accept h5 format so it needs to be saved as a pb.In looking through samples of adopting TensorFlow in ML.NET, this sample shows a TensorFlow model that is saved in a similar format to the SavedModel format - recommended by TensorFlow (and also recommended by ML.NET here "Download an unfrozen ..."). However when saving and loading the pb file into Netron I get this:
And zoomed in a little further (on the far right side),
As you can see, it looks nothing like it should.Additionally the input nodes and output nodes are... less
This may not be the type of question to ask on SO, but just wanted to hear what about other people have to say regarding what factors to consider in implementing machine-learning... moreThis may not be the type of question to ask on SO, but just wanted to hear what about other people have to say regarding what factors to consider in implementing machine-learning algorithms in a large enterprise environment.
One of my goals is to research industry machine-learning solutions that can be tailored to my company's specific needs. Being pretty much the only person who has a math background on my team and and who has done some background reading on machine-learning algorithms previously, I'm tasked with explaining/comparing machine-learning solutions in the industry. From what I've gleaned by googling around, it seems that:
a. Machine-learning and predictive analytics aren't exactly the same thing, so what's inherently different when a company offers predictive analytics software vs. machine-learning software? (e.g. IBM Predictive Analytics vs. Skytree Server)
b. A lot of popular terminology often gets entangled together, especially regarding Big Data, Hadoop, machine-learning, etc. Could... less
I've been reading a lot of articles that explain the need for an initial set of texts that are classified as either 'positive' or 'negative' before a sentiment analysis system... moreI've been reading a lot of articles that explain the need for an initial set of texts that are classified as either 'positive' or 'negative' before a sentiment analysis system will really work.
My question is: Has anyone attempted just doing a rudimentary check of 'positive' adjectives vs 'negative' adjectives, taking into account any simple negators to avoid classing 'not happy' as positive? If so, are there any articles that discuss just why this strategy isn't realistic?
How to save/restore a model after training? (26... moreThis question already has answers here:
How to save/restore a model after training? (26 answers)
Closed 3 years ago.
I'm relatively new to machine learning and the Tensorflow framework. I was trying to take my trained model heavily influenced by the code presented here, using the MNIST handwritten digit dataset and perform inferences on testing examples that I have created. However, I am doing the training on a remote machine with a GPU and am trying to save the data to a directory so that I can transfer the data and inference on a local machine
It seems that I was able to save some of the model with tf.saved_model.simple_save, however, I'm unsure of how to use the saved data to do inferencing and to use the data to make a prediction given a new image. It seems like there are multiple ways to save a model, but I am unsure of what the convention or of what the "correct way" is to do it with the Tensorflow framwork.
So far, this is the line that I think I would need, but am unsure if it is... less
