Comprehensive Foundation of Artificial Intelligence and Sequential Modeling
This document serves as a detailed technical primer on the core principles and architectures of modern Artificial Intelligence. It begins by defining the distinction between Artificial General Intelligence (AGI) and Narrow AI, outlining human-like cognitive abilities such as abstract reasoning and problem-solving. The content covers a broad spectrum of AI applications, including: Natural Language Processing (NLP): Key techniques such as tokenization, padding, and word embeddings for handling sequential text data. Generative AI & Task Analysis: Processes for sentiment analysis, entity extraction, summarization, and speech-to-text conversion. Neural Network Architectures: A deep dive into standard feed-forward networks versus sequential models. It specifically details Recurrent Neural Networks (RNNs) and their various architectures (one-to-many, many-to-one, etc.). Advanced Sequential Logic: An explanation of Long Short-Term Memory (LSTM) units, focusing on their specialized memory cells and gating mechanisms designed to solve the problem of long-term dependencies in data. Computer Vision & Specialized AI: High-level overviews of image classification, object detection (YOLO), and anomaly detection.
Document information
- Uploaded on
- March 18, 2026
- Number of pages
- 12
- Written in
- 2024/2025
- Type
- Class notes
- Professor(s)
- Dr. amit naik
- Contains
- All classes
