๐ But what is it?
๐ And what does it consist of?
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RAG helps bridge the gap between large language models and external data sources, allowing AI systems to generate relevant and informed responses by leveraging knowledge from existing documents and databases.
It involves a five-step process ๐
1๏ธโฃ Data Collection
The first step is gathering all the data needed for the application - user manuals, databases, FAQs, etc. For a customer support chatbot, this could include product documentation, troubleshooting guides, and common inquiries.
Vector databases are pivotal for Large Language Models (LLMs) due to their ability to handle high-dimensional vector data efficiently.
โถ๏ธ They optimize storage, retrieval, and management of vector embeddings crucial for LLM performance.
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โฆ They empower similarity searches vital for LLMs in tasks like semantic search and recommendation systems.
By finding the most similar vector embeddings within large datasets, they aid in delivering more accurate results.
โฆ Vector databases are scalable solutions for LLMs operating on massive datasets.
They're engineered to perform well even as data size scales up, making them indispensable for large-scale ML applications.
What is the difference between seasonality and cyclicality in time series forecastingโ
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๐งต
Seasonality and cyclicality are two essential concepts that play a crucial role in understanding patterns within time series data.
Let's start with seasonality! ๐๐โ๏ธ
1๏ธโฃ Seasonality represents a predictable pattern that repeats within a fixed time frame, often a year. It's influenced by external factors like weather, holidays, or cultural events.
๐ EXAMPLE: Think of the surge in ice cream sales during summertime! ๐ฆ๐
Have you ever wondered how ๐ฆ๐๐ฝ๐ฝ๐ผ๐ฟ๐ ๐ฉ๐ฒ๐ฐ๐๐ผ๐ฟ ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ๐ (SVM) can handle non-linear data?
The "๐๐ฒ๐ฟ๐ป๐ฒ๐น ๐ง๐ฟ๐ถ๐ฐ๐ธ" is a fascinating mathematical technique that allows efficient calculations and delivers powerful results!
Let's learn more about it ๐งต ๐
In SVM, the kernel trick is a clever way to perform complex calculations in a higher-dimensional feature space without explicitly transforming the original data into that space.
It's like finding a hidden pathway to handle non-linear relationships between data points.
Let's imagine we have a dataset with 2 classes of points that aren't linearly separable in a 2D space.
The kernel trick enables us to find a decision boundary, or hyperplane, that effectively separates these classes.
But without having to transform the data explicitly! ๐คฏ