All Questions

I recently read Fully Convolutional Networks for Semantic Segmentation by Jonathan Long, Evan Shelhamer, Trevor Darrell. I don't understand what "deconvolutional layers" do / how they work. The ...

I know that there is a possibility in Keras with the class_weights parameter dictionary at fitting, but I couldn't find any example. Would somebody so kind to ...

I'm a newbie to data science, and I do not understand the difference between the fit and fit_transform methods in scikit-learn. ...

I am trying out a multiclass classification setting with 3 classes. The class distribution is skewed with most of the data falling in 1 of the 3 classes. (class labels being 1,2,3, with 67.28% of the ...

One of the common problems in data science is gathering data from various sources in a somehow cleaned (semi-structured) format and combining metrics from various sources for making a higher level ...

My data set contains a number of numeric attributes and one categorical. Say, NumericAttr1, NumericAttr2, ..., NumericAttrN, CategoricalAttr, where ...

Referring to the Stanford course notes on Convolutional Neural Networks for Visual Recognition, a paragraph says: "Unfortunately, ReLU units can be fragile during training and can "die". For ...

I'm using Neural Networks to solve different Machine learning problems. I'm using Python and pybrain but this library is almost discontinued. Are there other good alternatives in Python?

I have been building models with categorical data for a while now and when in this situation I basically default to using scikit-learn's LabelEncoder function to transform this data prior to building ...

I have been using pandas for quite some time. But, I don't understand what's the difference between isna() and isnull(). And, ...

The key difference between a GRU and an LSTM is that a GRU has two gates (reset and update gates) whereas an LSTM has three gates (namely input, output and forget gates). Why do we make use of GRU ...

In the MNIST For ML Beginners they define cross-entropy as $$H_{y'} (y) := - \sum_{i} y_{i}' \log (y_i)$$ $y_i$ is the predicted probability value for class $i$ and $y_i'$ is the true probability ...

I have built my model. Now I want to draw the network architecture diagram for my research paper. Example is shown below:

When writing a paper / making a presentation about a topic which is about neural networks, one usually visualizes the networks architecture. What are good / simple ways to visualize common ...

I'm just starting to develop a machine learning application for academic purposes. I'm currently using R and training myself in it. However, in a lot of places, I have seen people using Python. What ...

I've been using SQL since 1996, so I may be biased. I've used MySQL and SQLite 3 extensively, but have also used Microsoft SQL Server and Oracle. The vast majority of the operations I've seen done ...

How could I split randomly a data matrix and the corresponding label vector into a X_train, X_test, X_val, y_train, y_test, y_val with Sklearn? As far as I know, ...

I'm currently working on implementing Stochastic Gradient Descent, SGD, for neural nets using back-propagation, and while I understand its purpose I have some ...

I am trying to run SVR using scikit learn ( python ) on a training dataset having 595605 rows and 5 columns(features) and test dataset having 397070 rows. The data has been pre-processed and ...

I'm just getting started with some machine learning, and until now I have been dealing with linear regression over one variable. I have learnt that there is a hypothesis, which is: $h_\theta(x)=\...

I am trying to get started learning about RNNs and I'm using Keras. I understand the basic premise of vanilla RNN and LSTM layers, but I'm having trouble understanding a certain technical point for ...

I am building a regression model and I need to calculate the below to check for correlations Correlation between 2 Multi level categorical variables Correlation between a Multi level categorical ...

Lots of people use the term big data in a rather commercial way, as a means of indicating that large datasets are involved in the computation, and therefore potential solutions must have good ...

Can someone practically explain the rationale behind Gini impurity vs Information gain (based on Entropy)? Which metric is better to use in different scenarios while using decision trees?

This is a small conceptual question that's been nagging me for a while: How can we back-propagate through a max-pooling layer in a neural network? I came across max-pooling layers while going through ...

I was starting to look into area under curve(AUC) and am a little confused about its usefulness. When first explained to me, AUC seemed to be a great measure of performance but in my research I've ...

The problem that I am dealing with is predicting time series values. I am looking at one time series at a time and based on for example 15% of the input data, I would like to predict its future values....

I got ValueError when predicting test data using a RandomForest model. My code: ...

What are the differences, if any, between a "data scientist" and a "machine learning engineer"? Over the past year or so "machine learning engineer" has started to show up a lot in job postings. ...

I am doing some problems on an application of decision tree/random forest. I am trying to fit a problem which has numbers as well as strings (such as country name) as features. Now the library, scikit-...

I've recently read Yan LeCuns comment on 1x1 convolutions: In Convolutional Nets, there is no such thing as "fully-connected layers". There are only convolution layers with 1x1 convolution kernels ...

I knew that Residual Network (ResNet) made He normal initialization popular. In ResNet, He normal initialization is used , while the first layer uses He uniform initialization. I've looked through ...

Problem Background: I am working on a project that involves log files similar to those found in the IT monitoring space (to my best understanding of IT space). These log files are time-series data, ...

This question is in response to a comment I saw on another question. The comment was regarding the Machine Learning course syllabus on Coursera, and along the lines of "SVMs are not used so much ...

From wikipedia, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and ...

Logic often states that by underfitting a model, it's capacity to generalize is increased. That said, clearly at some point underfitting a model cause models to become worse regardless of the ...

What is the right approach and clustering algorithm for geolocation clustering? I'm using the following code to cluster geolocation coordinates: ...

I am working on a data science project using Python. The project has several stages. Each stage comprises of taking a data set, using Python scripts, auxiliary data, configuration and parameters, and ...

I read somewhere that if we have features that are too correlated, we have to remove one, as this may worsen the model. It is clear that correlated features means that they bring the same information, ...

have been reading up a bit on LSTM's and their use for time series and its been interesting but difficult at the same time. One thing I have had difficulties with understanding is the approach to ...

I am trying to convert a list of lists which looks like the following into a Pandas Dataframe ...

I've been thinking about the Recurrent Neural Networks (RNN) and their varieties and Convolutional Neural Networks (CNN) and their varieties. Would these two points be fair to say: Use CNNs to break ...

I'm having trouble understanding the difference between equivariant to translation and invariant to translation. In the book Deep Learning. MIT Press, 2016 (I. Goodfellow, A. Courville, and Y. Bengio)...

Latent Dirichlet Allocation (LDA) and Hierarchical Dirichlet Process (HDP) are both topic modeling processes. The major difference is LDA requires the specification of the number of topics, and HDP ...

I am using TensorFlow for experiments mainly with neural networks. Although I have done quite some experiments (XOR-Problem, MNIST, some Regression stuff, ...) now, I struggle with choosing the "...

I see a many times in job description for data scientist asking for Python/Java experience and disregard R. Below is a personal email I received from chief data scientist of a company I applied for ...

What are the common/best practices to handle time data for machine learning application? For example, if in data set there is a column with timestamp of event, such as "2014-05-05", how you can ...

I often read that in case of Deep Learning models the usual practice is to apply mini batches (generally a small one, 32/64) over several training epochs. I cannot really fathom the reason behind this....

Suppose I build a neural network for classification. The last layer is a dense layer with Softmax activation. I have five different classes to classify. Suppose for a single training example, the <...

It looks like the cosine similarity of two features is just their dot product scaled by the product of their magnitudes. When does cosine similarity make a better distance metric than the dot product? ...