SBERT stands for Bidirectional Encoder Representations from Transformers.
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 what you need!
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, ...
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. 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 ...
The rock-paper-scissors dataset is a classic dataset used for computer vision and machine learning purposes.
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 ...
Introducing well-known Image datasets for developing machine learning algorithms.
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 ...
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Posting about some interesting tips in coding, developing machine learning models, and statistical analysis. I also try to solve some of the common issues in the python and R environment and share them.
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