This article is about building an unstructured data pipeline using RAG. A RAG (Retrieval-Augmented Generation) system works only if the…
IDEAS EMPRENDEDORAS (79) – Learning by Building in Open: From AI Stanford course CS231n to BIM Knowledge Creation
One of the most compelling aspects of Andrej Karpathy’s career is not only his technical excellence in artificial intelligence, but…
ARTIFICIAL INTELLIGENCE (26) – Natural Language Processing (6) PDFs as inputs
PDFs are a particularly challenging source for NLP because they are not designed as structured, machine-friendly data formats. A PDF…
ARTIFICIAL INTELLIGENCE (25) – Natural Language Processing (5) Fundamentals
Natural Languages are languages used by humans. The aim of Natural Language Processing is to talk to computers in Natural…
ARTIFICIAL INTELLIGENCE (24) – Deep learning (17) Building a Deep Learning APP
1. Objective The goal is to build a complete Deep Learning application, including: -Training a sentiment analysis model. -Saving the…
ARTIFICIAL INTELLIGENCE (23) – Natural Language Processing (4) Text Representations in NLP (3) Word embeddings
Word embeddings are a more advanced way to represent words compared to count vectors. Instead of just counting words, each…
Debe estar conectado para enviar un comentario.