Tokenization is the first step in most NLP tasks. It's essential because computers can't understand raw text; they need structured data. Tokenization helps convert text into a format suitable for further analysi
Both lemmatization andstemmingare text normalization techniques. In linguistics, lemmatization is closely related to stemming, as both strip prefixes and suffixes that have been added to a word's base form. Stemming is mainly used to map different forms of a word to a single form. It typically ...
a phrase, which allows it to thereby determine meaning and context. With text, the focus is to predict the next word. A transformer architecture does this by processing data through different types of layers, including those focused on self-attention, feed-forward, and normalization functionality....
What is Data Normalization in Vector Databases? Data normalization in vector databases involves adjusting vectors to a uniform scale, a critical step for ensuring consistent performance in distance-based operations, such as clustering or nearest-neighbor searches. Common techniques like min-max scaling...
The residual layer thoroughly checks the output transferred by the encoder to ensure no two values are overlapping neural network's activation layer is enabled, predictive power is bolstered, and the text is understood in its entirety. Tip:The output of each sublayer (x) after normalization is...
Layer normalization is like a reset button for each layer in the model, ensuring that things stay balanced throughout the learning process. This added stability allows the LLM to generate well-rounded, generalized outputs, improving its performance across different tasks. ...
The retrieval stage in an RAG architecture is where the magic happens. Here, the system efficiently locates relevant information from the indexed data to enhance the LLM generation capabilities. This process ensures that the user’s query (often called a prompt in NLP) is processed in the same...
All in all, serializability is a property of a system that describes how different processes operate on shared data. In a database management system, this can be accomplished by locking data so that no other process can access it while it is being read or written. Serializability guarantees th...
processes. This protects the model from extreme data values or unusual variations that can distort the transformation process and result in poor output. Additional normalization techniques, such as residual connections, are used to handle the problem of vanishing gradients where the model is difficult ...
Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referre...