In game theory and economic theory, a zero-sum game is a mathematical representation of a situation in which each participant’s gain or loss of utility is exactly balanced by the losses or gains of the utility of the other participants. If the total gains of the participants are added up and the total losses are subtracted, they will sum to zero. …


Bidirectional Encoder Representations from Transformers (BERT) is a Transformer-based machine learning technique for natural language processing (NLP) pre-training developed by Google.


It is easy to create a dataset, but to get a gold medal follow these recommendations based on my experience working with this dataset: [U.S. Gasoline and Diesel Retail Prices 1995–2021](https://www.kaggle.com/mruanova/us-gasoline-and-diesel-retail-prices-19952021)


The dot product is a scalar. The dot product of two vectors gives you the value of the magnitude of one vector multiplied by the magnitude of the projection of the other vector on the first vector.


Q-learning is a model-free reinforcement learning algorithm to learn quality of actions telling an agent what action to take under what circumstances. It does not require a model (hence the connotation “model-free”) of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations.


In statistics, a sampling distribution or finite-sample distribution is the probability distribution of a given random-sample-based statistic. If an arbitrarily large number of samples, each involving multiple observations (data points), were separately used in order to compute one value of a statistic (such as, for example, the sample mean or sample variance) for each sample, then the sampling distribution is the probability distribution of the values that the statistic takes on. In many contexts, only one sample is observed, but the sampling distribution can be found theoretically.


Hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm.


k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the clusterwith the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. k-means clustering minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances. …


An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. This curve plots two parameters:

  • True Positive Rate
  • False Positive Rate

True Positive Rate (TPR) is a synonym for recall and is therefore defined as follows:


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Each bar represents a digit, and the height of the bar is the percentage of numbers that start with that digit.

Mau Ruanova

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