This part of blog explores powerful modeling techniques for time series data analysis, focusing on Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA) models, as well as Vector Autoregression (VAR) models.
The fundamentals of time series analysis, beginning with an understanding of different types of time series data to various modeling techniques including autoregressive and moving average models are discussed.
Time series analysis is a systematic approach to studying how data changes over time. By examining patterns, trends, and behaviors within the data, insights can be uncovered and predictions can be made. This involves several steps that helps to reveal valuable insights.
Dive into the evolution of word embeddings, from CBOW to Skip-gram, and witness a quantum leap with transformers. Explore the architecture of transformers, including BERT and GPT, and grasp the power of contextualized embeddings.
In this exploration of word embeddings and foundational techniques in NLP, the focus is on converting textual data into numerical representations for computational understanding. They represent words in a continuous vector space, capturing semantic relationships and contextual nuances.
Uncover the evolution of AI in reshaping technology and human interactions amid the surge in data availability. Explore the pivotal role of fuzzy logic in navigating real-world data complexities, adeptly managing linguistic variables and degrees of truth for uncertain data.
