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. The model is pre-trained on large amounts
of text data, such as Wikipedia articles, to learn the relationships between words and sentences.
Self-attention is a mechanism that allows a neural network to selectively weigh the importance of
different parts of an input sequence when making predictions or generating outputs. It is a key
component of transformer models, which have revolutionized the field of natural language processing
(NLP) in recent years. However, ...
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. The goal of a machine learning model is to minimize this
difference, or “loss”, in order to make accurate predictions.
The choice of a loss ...
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. The images in an
Image data refers to a collection of digital images that are stored and processed by computers.
MNIST is a commonly used dataset for image classification tasks in machine learning and computer
vision. It consists of 70,000 grayscale images of handwritten digits (0 9) and their corresponding
labels. These images are pre-processed and ...