In part one of this blog series , I explored the motivation behind developing a personal recommendation system. The main goals are to learn how recommendation systems work and to build a tool that helps me find interesting blog posts and articles from feeds where only 1 in 20 posts might match my content interests. If you are interested in the technical implementation, the complete codebase is available in this github repository .| Saeed Esmaili
Fine-tuning the Phi 1.5 model on the BBC News Summary dataset for Text Summarization using Hugging Face Transformers.| DebuggerCafe
Text Summarization using T5: Uncover advanced NLP techniques and applications with the transformative T5 model, shaping the future of concise, AI-driven content| LearnOpenCV – Learn OpenCV, PyTorch, Keras, Tensorflow with code, & tutorials