All Questions

Please redirect me to another stack exchange if this isn't the appropriate forum. I am interested in finding a neural network architecture that can detect distance between two inputs. As an example, ...

I have a table like this: Date Revenue Outlier Month 01/2018 333 No M0 02/2018 125 No M1 03/2018 136 No M2 04/2018 123 No M0 05/2018 496 No M1 06/2018 134 No M2 07/2018 223 Yes M0 08/2018 145 ...

Is it possible to detect fileless malware attacks with a machine learning model or with any other data science application? Fileless malware exists only in a computer’s random-access memory (RAM), ...

I am training an NER pipeline - It was pretrained. My label appears sparsely - Once every 100 or 200 sentences. Do I have to train my pipelines with ALL sentences or can I speed the training up by ...

In binary image segmentation, for given a set of images, it's true mask and predicted mask. How to compute dice score?, should I compute dice score for each image separately and then find mean across ...

Looking for modern algorithms using NN Language Model implementations addressing polysemy in NLP tasks, including text classification, question answering and topic modeling. Transfer/Zero-short ...

I need to make a regression model to estimate data values in future. Train set contains occasional spikes that make my model less accurate, thus I'm trying to locate and remove them. I've used ...

I have a txt file (here) and i have tried to plot it with pandas, xarray of python I tried with ...

I'm evaluating the performance of a classifier regarding its false negatives. The classifier performed over 9090 samples, from which 9000 were labeled as negative. I randomly chose 800 samples (out of ...

I am building a model to predict one label by taking one feature as an input. The two variables seems to be strongly correlated. I wanted to build a (sequential) Neural Network model with Keras in ...

I have hourly data regarding the number of visits to the website. A single record consists of date, time(0-23) and number of visits. In a separate table I have data about the display of advertising ...

I have been using the 3 metrics independently for a while now, but trying to figure out if they are actually 3 separate things (with similar-looking definitions/names) or there is some underlying ...

I needed to use cross-validation in the Neural network. I used kf = KFold(n_splits=5, shuffle=True) and ...

For the above table, midpoints for possible split points are 22.5 and 35. I have calculated the entropy and gain for each value and 35 had the minimum Entropy and highest gain. Is it correct ? Entropy ...

I have created a convolutional neural network model which classifies malware based on the Malimg dataset. Below is the model and the results from the first run: ...

I have a dataset of 535 classes (258 194 images) and I want to divide it into train, validation and test set. I am a new learner in image classification with CNN, should I do that manually or is there ...

Consider a target vector like ...

I am trying to use SSIM as loss value for my Keras Sequential model. The output value is a set of 6 images - (6, 32, 28, 3). I want to implement a custom loss function for the model.fit() function but ...

I am new to AI and I don't get really well Precision and Recall for my case. I have to create an AI that classify mutliples objects in order to accept them or not inside a machine. The problem is that ...

I am a beginner to machine learning. I'm building a tennis stroke classification system using action recognition. The dataset consists of different stroke videos. In all the tutorials I referred, the ...

Say I have a element that base on a certain condition (1, 2, 3 or 4), it chooses a decision (A,B or C). What diagrams can be used to present this data if I have more than one thousand elements so that:...

I am building a supervised machine learning model which (for example) predicts heart failure (yes/no). I have two datasets from 2 different labs A and B, which both have decent distribution, aka it's ...

I need to classify some domain specific images by analysing their color distribution. I have annotated data; this last classification step is supervised. After some preprocessing and masking and other ...

I have a feature with data creation dates. I have normalized them all to the same format and split them to 'day', 'month' and 'year' columns. But now I have a question. Should I apply normalization or ...

I'm testing SARIMA on the first 4 months (observations are in days, total 120 days) of this dataset: https://api.covidtracking.com/v1/states/ny/daily.cs I'm not sure what value I should assign to the ...

What are the different non linear regression techniques and how many of them are available on scikit learn?

Why is a coupling layer called so, in NICEand RealNVP density estimation? Does it have anything to do with the coupling of probability distributions? What is the intuitive goal of using these layers?

Given: set of images Labels in string format each one. Also I've given a set of Categories, also in string. ($Images \neq Categories $) Goal: I need to map given labels to given categories to "...

I'm having difficulty searching for the original published paper proposing k-Means as an algorithm. I have been inspired to find it as reference for similar work, inspired by this TowardsDataScience ...

I have a dataset with 18000 rows and 192 columns. I have a specific column with more than 2000 rows missing. I've tried some types of imputations but they just take to long or just seem not good ...

I have been working on some graph embedding deep learning algorithms for anomaly detection using some log files. My issue is that I need to use splunk found on a system different than my model make ...

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I recently stumbled upon these layers, and it's an interesting idea, but I'm not sure what one would do with it. What is the application of a Convolutional layer that doesn't share its weights? Where ...

I'm trying to understand LDA model by reading through implementations of the algorithm. Many implementations update alpha during training iterations with codes like: ...

Given a correlation matrix M and after an event, M changes. What is the best way to analyse the change? Verify significance (presumably T-test, ANOVA..) Currently looking at the %, count & average ...

I recently participated in some Machine Learning competition where we were asked to decide whether a rider should accept or not a course (~2k riders and ~140k courses). It came up that some of the ...

This is a follow up question to: What's the difference between fit and fit_transform in scikit-learn models? I want to know why should we use fit at all when we ...

I am building a swimming stroke classification system using a CNN. l I assume each stroke contains 3 steps/classes ('Ready', 'Impact', 'Finish'). I want to train a model which will predict whether the ...

The "momentum" adds a little of the history of the last weight updates to the actual update, with diminishing weight history (older momentum shares get smaller). Is it significiantly ...

I was thinking of doing a job including regression firstly, then doing the classification. I read lots of sources that are saying it is Semi-Supervised learning ...

I am currently trying to build a 3D-CNN model to predict video quality. When I trained the model with a small dataset (300 videos) it predicts different values, but when I use a huge dataset for ...

In statistics, a sequence of random variables is homoscedastic if all its random variables have the same finite variance. This is also known as homogeneity of variance. (Wikipedia) Is this assumption ...

PoseNet is state of art approach and I am using it for a pose estimation. I read on the internet that pose net uses models like MobileNet or VGG etc. I would like to know that is PoseNet itself a ...

I have a dataframe with 3 columns: job_type (5 categories), age_group (5 categories) and answer (0, 1, 2 or 3) job_type | age_group | answer By ...

Assume there's a customer dataset with monthly installments repaying goods purchased in advance. The objective is to build a model predicting who is going to be bad debtors and who can afford to buy ...

I am working on an EEG signal classification problem. My dataset consists of EEG signals stored as 19X30000 NumPy arrays. Each row represents a single channel. For now, I am converting each of the ...

I have a training dataset of 10000 pictures and a test dataset of 15000 pictures. There are 23 types of birds. First of all, I imported the necessary ...

The point of EarlyStopping is to stop training at a point where validation loss (or some other metric) does not improve. If I have set ...

I'm reading this paper about mutual information in infoGAN infoGAN_paper_link and already have the code to run it. I pretty much found code for it which is fine and dandy except for the fact that I ...

The title may be confusing but suppose I were to build Transformer Neural Network with a masking network that utilizes multi-head attention (like that in SepFormer), would adding self-attention in the ...