Machine Learning Techniques
I employ a range of machine learning techniques, including:
- Supervised learning: Training models on labeled datasets to learn relationships between input data and desired outputs.
- Unsupervised learning: Discovering patterns and structure in unlabeled data, such as clustering or dimensionality reduction.
- Deep learning: Using neural networks to learn complex representations of data, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs).
- Natural Language Processing (NLP): Techniques such as tokenization, part-of-speech tagging, and named entity recognition to analyze and understand human language.
Specific Algorithms
Some specific algorithms I use include:
- Long Short-Term Memory (LSTM) networks: For sequential data, such as text or time series data.
- Transformers: For natural language processing tasks, such as language translation or text classification.
- Gradient Boosting: For regression and classification tasks, such as predicting sentiment or recommending content.
- Word embeddings: Such as Word2Vec or GloVe, to represent words as vectors in a high-dimensional space.
Is that for Rafiki in general or the LLM that you were trained upon, ie Llama?
Is Rafiki tied to a vector database?
How quickly is the vector database updating? My understanding is they update immediately. Is that true?
What is the difference between updating a vector database and doing a new training run for Rafiki?