In machine learning, a “splitter” refers to a tool or technique that is used to split a dataset into separate subsets for training and testing a machine learning model. Splitting a dataset is a common practice in machine learning to avoid overfitting the model to the training data and to…

In machine learning, a splitter is a function or module used to split a dataset into two or more subsets for different purposes. Splitting a dataset is an essential step in many machine-learning tasks, such as model training, validation, and testing. The most common type of splitter is the train-test…

Multitask learning (MTL) is a machine learning approach that involves training a model to perform multiple tasks simultaneously using the same input features. In other words, instead of introducing a separate model for each task, MTL trains a single model that can perform multiple tasks. The idea behind MTL is…

Parameters In machine learning, a model parameter refers to a configuration variable that is internal to the model and is used to make predictions on new data. Model parameters are learned from training data, which is input to a machine learning algorithm. The algorithm uses the training data to adjust the…

Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) and computer science that focuses on enabling computers to process, understand, and generate human language. …

BERT stands for Bidirectional Encoder Representations from Transformers. It is a state-of-the-art natural language processing (NLP) model developed by Google researchers in 2018. BERT uses a type of deep learning architecture called Transformers, which allows it to understand the context and meaning of words in a sentence or passage. …

Self-attention is a mechanism used in machine learning, particularly in natural language processing (NLP), that allows a model to weigh the importance of different parts of an input sequence when making predictions or generating outputs. Introduction Self-attention is a mechanism that allows a neural network to selectively weigh the importance of…

In machine learning, a loss function is a mathematical function that measures how well a machine learning model is able to make predictions. The loss function compares the predicted output of the model to the true output and produces a score that indicates how different the two are. …

An image dataset is a collection of digital images that are organized and labeled for use in machine learning and computer vision tasks. These datasets are used to train and test computer vision algorithms, such as object recognition, image classification, and segmentation. …