This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive AI systems.
Introducing Aeneas, the first AI model for contextualizing ancient inscriptions, designed to help historians better interpret, attribute, and restore fragmentary texts. It reasons across thousands of Latin inscriptions, retrieving textual and contextual parallels to aid in historical research.
This article provides a beginner-friendly explanation of attention mechanisms and transformer models, covering sequence-to-sequence modeling, the limitations of RNNs, the concept of attention, and how transformers address these limitations with self-attention and parallelization.
This article provides a comprehensive guide on the basics of BERT (Bidirectional Encoder Representations from Transformers) models. It covers the architecture, use cases, and practical implementations, helping readers understand how to leverage BERT for natural language processing tasks.
A tutorial on using LLM for text classification, addressing common challenges and providing practical tips to improve accuracy and usability.
This article explains BERT, a language model designed to understand text rather than generate it. It discusses the transformer architecture BERT is based on and provides a step-by-step guide to building and training a BERT model for sentiment analysis.
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
Case study on measuring context relevance in retrieval-augmented generation systems using Ragas, TruLens, and DeepEval. Develop practical strategies to evaluate the accuracy and relevance of generated context.
This tutorial covers fine-tuning BERT for sentiment analysis using Hugging Face Transformers. Learn to prepare data, set up environment, train and evaluate the model, and make predictions.
• A beginner's guide to understanding Hugging Face Transformers, a library that provides access to thousands of pre-trained transformer models for natural language processing, computer vision, and more.
• The guide covers the basics of Hugging Face Transformers, including what it is, how it works, and how to use it with a simple example of running Microsoft's Phi-2 LLM in a notebook
• The guide is designed for non-technical individuals who want to understand open-source machine learning without prior knowledge of Python or machine learning.
• The guide covers the basics of Hugging Face Transformers, including what it is, how it works, and how to use it with a simple example of running Microsoft's Phi-2 LLM in a notebook
• The guide is designed for non-technical individuals who want to understand open-source machine learning without prior knowledge of Python or machine learning.
