The "target" is what a predictive model aims to predict or estimate based on the input data –also called the dependent variable, response variable, outcome variable, or label.

Target

Tableau is widely used data visualization and business intelligence (BI) software that allows users to connect, visualize, and share data to facilitate understanding and decision-making.

Tableau

A sequence of data points collected or recorded over a period of time, typically at equally spaced intervals –common in domains like finance, economics, meteorology, etc.

Time Series

Time series data is a type of data where observations or measurements are collected and recorded over time, with each data point associated with a specific timestamp.

Time Series Data

A train-test split is common in machine learning for evaluating the performance of a model. Data is divided into two subsets: for training (train), and for evaluating (test).

Train Test Split

The t-distribution is a probability distribution for estimating population parameters when the sample size is small and the population standard deviation is unknown.

T-distribution

A t-test is a statistical test used to compare the means of two groups and determine whether there is a significant difference between them.

T-test

In the context of machine learning and deep learning, a tensor is a multi-dimensional array or a mathematical object that generalizes the concept of scalars, vectors, and matrices.

Tensor

A Tensor Processing Unit (TPU) is a specialized hardware accelerator designed by Google for machine learning workloads, particularly those associated with deep neural networks.

Tensor Processing Unit (TPU)

Type I error, also known as a "false positive" or "alpha error," occurs in statistical hypothesis testing when a true null hypothesis is incorrectly rejected.

Type I Error

Type II error, also known as a "false negative" or "beta error," occurs in statistical hypothesis testing when a false null hypothesis is not rejected.

Type II Error

TensorFlow is an open-source machine learning framework. It is widely used for building and training machine learning models –particularly neural networks.

What is TensorFlow?

TensorFlow

TensorBoard is a web-based visualization tool that comes with TensorFlow. It helps users understand, optimize, and debug machine learning models during the training process.

TensorBoard

In machine learning, the term "test loss" typically refers to the performance of a trained model on a separate dataset that it has not seen during the training phase.

Test Loss

In machine learning, a test set is a separate dataset used to assess the performance and generalization ability of a trained machine learning model.

Test Set

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Training Loss

A training set is a random subset of a dataset used to train a machine learning model. The model learns patterns and relationships within the data to help it make predictions.

Training Set

Tokenization is the process of breaking down a text into individual units, known as "tokens". These tokens could be words, subwords, or even characters (but are usually words).

Tokenization

Transfer learning is a machine learning technique where a model trained on one task is adapted to perform a different, but related, task. Pre-trained models have a number of advantages.

Transfer Learning

A true negative (TN) occurs when the model correctly identifies instances of the negative class. The model successfully recognizes cases where the true class is negative.

True Negative

True Negative (TN)

A true positive (TP) occurs when the model correctly identifies instances of the positive class. The model successfully recognizes cases where the true class is positive.