As far as I know, there is no package available for Rand Index in python while for Adjusted Rand Index you have the option of using sklearn.metrics.adjusted_rand_score(labels_true, labels_pred). ...

Which are the fundamental criterias for using supervised or unsupervised learning? When is one better than the other? Is there specific cases when you can only use one of them? Thanks

All or nearly all of the papers using dropout are using it for supervised learning. It seems that it could just as easily be used to regularize deep autoencoders, RBMs and DBNs. So why isn't dropou...

In supervised learning I have the typical train/test split to learn the algorithm, e.g. Regression or Classification. Regarding unsupervised learning, my question is: Is train/test split necessary ...

I am trying to create program that cluster documents using hierarchical agglomerative clustering, and the output of the program depends on cutting the dendrogram at such a level that I get maximum ...

I am trying to use BERT for a document ranking problem. My task is pretty straightforward. I have to do a similarity ranking for an input document. The only issue here is that I don’t have labels -...

I have a database of images that contains identity cards, bills and passports. I want to classify these images into different groups (i.e identity cards, bills and passports). As I read about that,...

On my project I have used k-means to classify data between groups, but I have a problem with the computation of the k-means from Scikit-learn - it was very slow. I need to boost it. I have tried ...

I am attempting to create a genetic algorithm to train a neural network, with the goal of playing the game snake. The problem I am having is that the fitness of the generations isn't improving, it...

I'm trying to implement an unsupervised ANN using Hebbian updating in Keras. I found a custom Hebbian layer made by Dan Saunders here - https://github.com/djsaunde/rinns_python/blob/master/hebbian/...

Is there any way in Keras to specify a loss function which does not need to be passed target data? I attempted to specify a loss function which omitted the y_true parameter like so: def custom_lo...

I have a large data set 45421 * 12 (rows * columns) which contains all categorical variables. There are no numerical variables in my dataset. I would like to use this dataset to build unsupervised ...

I need to perform clustering without knowing in advance the number of clusters. The number of cluster may be from 1 to 5, since I may find cases where all the samples belong to the same instance, o...

I'm trying to build a CNN for an image-to-image translation application, the input of the model is an image, and the output is a confidence map. There are no labeled confidence as the ground truth ...

In terms of artificial intelligence and machine learning, what is the difference between supervised and unsupervised learning? Can you provide a basic, easy explanation with an example?

I was learning about non-linear clustering algorithms and I came across this 2-D graph. I was wondering which clustering alogirthm and combination of hyper-parameters will cluster this data well. ...

I recently studied about supervised learning and unsupervised learning. From theory, I know that supervised means getting the information from labeled datasets and unsupervised means clusteri...

I have done clustering using Kmeans using sklearn. While it has a method to print the centroids, I am finding it rather bizzare that scikit-learn doesn't have a method to find out the cluster lengt...

Is there a common online algorithm to classify news dynamically? I have a huge data set of news classified by topics. I consider each of that topics a cluster. Now I need to classify breaking news....

Topic modeling identifies distribution of topics in a document collection, which effectively identifies the clusters in the collection. So is it right to say that topic modeling is a technique to d...

I've had encouraging results clustering a set of entity names using scikit-learn's affinity propagation implementation, with a modified Jaro-Winkler distance as the similarity metric, but my cluste...

A dendrogram is a data structure used with hierarchical clustering algorithms that groups clusters at different "heights" of a tree - where the heights correspond to distance measures between clust...

I just finished the Bayesian Analysis in Python book by Osvaldo Martin (great book to understand bayesian concepts and some fancy numpy indexing). I really want to extend my understanding to bayes...

I have a data frame representing a time series like for example: timestamp: 1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25|26|27|28... value: 0|0|3|6|3|3|6|3|3|6 |3 |0 |0 |0 |1...

According to my understanding, Distant Supervision is the process of specifying the concept which the individual words of a passage, usually a sentence, are trying to convey. For example, a datab...