Tags
A tag is a keyword or label that categorizes your question with other, similar questions. Using the right tags makes it easier for others to find and answer your question.
Image preprocessing are the steps taken to format images before they are used by model training and inference. This includes, resizing, orienting, and color corrections.
Preprocessing is required t…
168 questions
Activation function is a non-linear transformation, usually applied in neural networks to the output of the linear or convolutional layer. Common activation functions: sigmoid, tanh, ReLU, etc.
167 questions
Graphics Processing Units (GPUs) within the context of Machine Learning often refer to the hardware requirements, design considerations, or level of parallelization for implementing and running variou…
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167 questions
For questions about models designed for generating new data (or generating samples from a probability distribution).
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Jupyter is a collection of environments and protocols for interactive computing. It supports many languages and kernels, and works with frontends including the web application Jupyter Notebook. Jupyte…
159 questions
MATLAB is a high-level language and interactive programming environment for numerical computation and visualization developed by MathWorks. Don’t use both the [matlab] and [octave] tags, unless the qu…
158 questions
Use for questions about graphing a dataset into a visualisation (e.g. line plot, histogram or pie chart). Visualisation and plotting is an important EDA tool as well as presenting the results of a dat…
156 questions
For question regarding distance between distributions or variables, such as Euclidean distance between points in n-space.
152 questions
For questions which concern getting started in Data Science or any of its related subdomains.
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150 questions
Hyperparameters of a model are the kind of parameters that cannot be directly learned during training but are set beforehand. Hyperparameters can define, for example, the complexity of the model or it…
148 questions
Language models are used extensively in Natural Language Processing (NLP) and are probability distributions over a sequence of words or terms.
148 questions
A topic model describes text from a large corpus as a probability distribution over topics which are probability distributions over words. There are quantified contributions from all topics to a speci…
147 questions
Bayesian statistics is a statistical paradigm that contrasts with that of frequentist statistics. Bayesian methods rely on prior information do determine the degree of belief in the probability of a v…
145 questions
Information Retrieval is an area of study concerning with retrieving documents, information or metadata from a collection of unstructured or semi-structured data.
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NLTK is a free, open-source natural language processing toolkit for python. It is used primarily for text processing applications and includes libraries specifically made for classification, tokenizat…
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tf–idf (term frequency–inverse document frequency), is a numerical statistic using in nlp that is intended to reflect how important a word is to a document in a collection or corpus. It is often used…
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136 questions
The Spark Python API (PySpark) exposes the apache-spark programming model to Python.
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Latent Dirichlet Allocation (LDA) is an algorithm in the field of topic modeling.
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126 questions
YOLO, or You Only Look Once, is a real-time object detection algorithm
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Ranking is ordering data from highest to lowest or
*vice versa.* For questions about *constructing* scores to use in ranking, please use the "valuation" tag, too.
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Data imputation is the process of replacing missing data with substituted values. This could involve statistically representative data filling (e.g. local averages) or simply replacing the missing dat…
121 questions
Hadoop is an Apache open-source project that provides software for reliable and scalable distributed computing. The project itself includes a variety of other complementary additions.
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Kaggle is an online community for data scientists and machine learning practitioners owned by Google.
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In machine learning, grid search refers to multiple runs to find the optimal value of parameter(s)/hyperparameter(s) of a model, e.g. mtry for random-forest or alpha, beta, lambda for glm, or C, kerne…
116 questions