Vector artwork is art that's made up of vector graphics. These graphics are points, lines, curves and shapes that are based on mathematical formulas. When you scale a vector image file, it isn't low resolution and there's no loss of quality, so it can be sized to however large or sm...
vector to a given query vector. Indexes are great for applications that require fast and accurate similarity searches, such as a recommendation engine. In contrast, vector databases are where organizations store vectors for retrieval and analysis. An enterprise-class vector database delivers useful ...
所有这些新的应用都依赖于矢量嵌入(vector embeddings),这是一种数据表示方式,其中含有语义信息,对人工智能获得理解和保持长期记忆至关重要,它们可以在执行复杂任务时加以利用。 Embeddings 是由人工智能模型(如大型语言模型)生成的,具有大量的属性或特征,使得它们的表示方法在管理上具有挑战性。在人工智能和机器学习的背...
A vector database is an organized collection of vector embeddings that can be created, read, updated, and deleted at any point in time.
A vector database stores, manages and indexes high-dimensional vector data to be stored as arrays of numbers called “vectors,” clustered based on similarity.
Vectorization, then, is the process of using these vector registers, instead of scalar registers, in an attempt to make the program run faster. In a perfect world, our example loop would execute 4 times faster.Vectorization can be performed in two ways:...
Vector search, sometimes referred to as vector similarity search, is a technique that uses vectors -- numerical representations of data -- as the basis to conduct searches and identify relevance. A vector, in the context of a vector search, is defined as a set of numbers mathematically compute...
Vector artwork is art that's made up of vector graphics. These graphics are points, lines, curves and shapes that are based on mathematical formulas. When you scale a vector image file, it isn't low resolution and there's no loss of quality, so it can be sized to however large or sm...
All ML.NET algorithms look for an input column that's a vector. By default, this vector column is calledFeatures. That's why the house price example concatenated theSizecolumn into a new column calledFeatures. C# varpipeline = mlContext.Transforms.Concatenate("Features",new[] {"Size"}) ...
All ML.NET algorithms look for an input column that's a vector. By default, this vector column is calledFeatures. That's why the house price example concatenated theSizecolumn into a new column calledFeatures. C# varpipeline = mlContext.Transforms.Concatenate("Features",new[] {"Size"}) ...