This project is an end-to-end recommendation pipeline and framework designed for building generative recommendation engines. It utilizes large language models and vector search to facilitate personalized item discovery and produces item recommendations through tokenization and semantic alignment. The system integrates a generative recommendation engine with a multi-objective ranking system and a vector search retrieval pipeline. These components allow for the use of reasoning frameworks to perform inference over items and the application of re-ranking algorithms to optimize the diversity of t
Gorse is a personalized recommendation engine server and machine learning pipeline designed to suggest items to users based on their behavior and preferences. It operates as a distributed system that separates training, candidate generation, and serving nodes to support high-throughput workloads. The system utilizes a multi-stage recommendation pipeline to refine results through retrieval, scoring, and reranking. It generates personalized suggestions using collaborative filtering, matrix factorization, and item-to-item similarity models, while also providing non-personalized and fallback reco
This project is a deep learning tutorial series and educational curriculum designed to teach PyTorch fundamentals. It serves as a structured training guide for mastering neural network architecture, automatic differentiation, and the use of tensors and dynamic computation graphs. The curriculum focuses on practical implementations, specifically guiding the development of recommendation systems, advertising models, and interest networks to predict user preferences. It also provides instructional content for time series forecasting and processing sequential data. The material covers a broad ra
This repository is a collection of practical deep learning implementations and examples built using the TensorFlow framework. It provides a variety of neural network architectures focusing on natural language processing, recommendation systems, reinforcement learning, and time series prediction. The project features a range of specialized models, including sequence-to-sequence and transformer architectures for text processing, and factorization machines for personalized ranking and retrieval. It also includes implementations of reinforcement learning agents using actor-critic and policy gradi