Vector embeddings are a powerful tool in natural language processing (NLP) that allows us to represent words, phrases, and even entire documents as vectors of numbers. These vectors can then be used in a variety of NLP tasks, such as sentiment analysis, machine translation, and text classification. In this blog post, we will explore vector embeddings in the context of large language models (LLMs), which are a type of neural network that have revolutionized NLP in recent years. We will cover the basics of vector embeddings, including how they are created and how they can be used in LLMs. We will also provide technical details, equations, and code examples where necessary. What are Vector Embeddings? Vector embeddings are lists of numbers that represent some kind of data, such as words, phrases, or images. In the context of NLP, vector embeddings are used to represent words and phrases as vectors of numbers. The idea behind vector embeddings is to capture the meaning of a word or phrase

Graph Neural Networks (GNNs) are a class of deep learning models designed to process and analyze graph-structured data. GNNs leverage the inherent structural information of graphs to learn powerful node and graph representations, enabling them to capture complex dependencies and propagate information effectively across the graph Here, we will explore the capabilities of GNNs and their applications in various machine-learning tasks. Capabilities of GNNs GNNs offer several advantages in handling various machine learning tasks, including: Node Classification: GNNs can accurately classify nodes in a graph based on their features and the relationships they have with other nodes. Link Prediction: GNNs can predict missing or future links in a graph, enabling them to model dynamic relationships and make accurate predictions. Graph Classification: GNNs can classify entire graphs based on their structural properties and the features of their nodes and edges. Community Detection: GNNs can ident

Sorting algorithms are essential tools in computer science and data processing. They allow us to organize and arrange data in a specific order, making it easier to search, analyze, and manipulate. One important aspect of sorting algorithms is their stability, which refers to the preservation of the relative order of equal elements during the sorting process. In this article, we will explore a new stable sorting algorithm called Quadsort, which belongs to the merge sort family and offers improved performance compared to other popular sorting algorithms 1. Stable Sorting Stable sorting algorithms are particularly useful when sorting data that contains multiple elements with the same key value. In such cases, a stable sorting algorithm will preserve the original order of these elements, ensuring that they appear in the same order in the sorted output as they did in the input. This property is crucial for many applications, such as sorting a list of people by their last names and then by t