Traditional databases work with storing strings, numbers, etc in rows and columns. When querying from traditional databases, we are querying for rows that match our query. However, vector databases work with vectors rather than strings, etc. Vector databases also apply a similarity metric which is...
Once the closest vectors are identified at the bottom layer, these points translate back to actual data, like images or music, representing your search results. Scalability Vector databases often deal with datasets that comprise billions of high-dimensional vectors. This data isn't just large in v...
Vector embeddings are the core component of enabling machine learning and AI. Once data is turned into vectors we need to store all the vectors in a highly scalable, highly performant repository called avector database. Once data has been transformed and stored as vectors that data can now pow...
In order to search and retrieve data from a set of embeddings, we need to define a method to compare two vectors. This is often called a similarity measure or similarity metric. These metrics determine that two vectors are nearly the same by comparing the distance or angle between them. Com...
What is Mathematics - Mathematics is the study of numbers, shapes and patterns. The word comes from the Greek word mathema meaning science, knowledge, or learning.Mathematics can be broadly grouped into the following branches:Arithmetic:It is the olde
s vectors are then used to do semantic searches in a vector database for an exact match or the top-K most similar vectors along with their corresponding data chunks, which are placed into the context of the prompt before sending it to the LLM. LangChain or LlamaIndex are popular open-...
Data Center Embedded Systems Jetson DRIVE AGX Clara AGX Application Frameworks AI Inference - Triton Automotive - DRIVE Cloud-AI Video Streaming - Maxine Computational Lithography - cuLitho Cybersecurity - Morpheus Data Analytics - RAPIDS Generative AI - NeMo Healthcare - Clara High-Pe...
Instead of the n-gram approach, we can try awindow-based neural language model,such asfeed-forward neural probabilistic language modelsandrecurrent neural network language models.This approach solves the data sparsity problem by representing words as vectors (word embeddings) and using them as input...
What is deep learning? Discover how it can be used to train a computer to perform human-like tasks, such as recognizing speech or marking predictions.
One method in which researchers are able to provide empirical support for their research is through the employment of statistics. Statistics refers to a system of numerical analysis of qualitative and quantitative data which may frequently be found in science. This evaluation can provide a large ...