Projects sharing features with Fun Rec

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

X-algorithm is a modular recommendation engine framework designed to orchestrate personalized content feeds. It functions as a machine learning ranking system that manages the end-to-end lifecycle of content delivery, from initial candidate retrieval to final display ordering. The system distinguishes itself through a multi-stage pipeline that integrates vector-based similarity search with transformer-based engagement prediction. By mapping user history and content features into high-dimensional embeddings, it performs rapid approximate nearest neighbor searches to identify relevant items. Th

LightFM is a Python recommendation library and machine learning framework designed to predict user preferences. It implements a hybrid recommendation engine that combines collaborative filtering with content filtering by integrating user-item interaction data with descriptive metadata. The system utilizes hybrid matrix factorization to learn latent representations of users and items. It is specifically designed to handle implicit feedback, utilizing specialized loss functions such as Weighted Approximate Rank Pairwise and Bayesian Personalized Ranking to optimize item preferences for datasets

Surprise is a Python library for building and analyzing recommendation systems. It provides a comprehensive toolkit for implementing collaborative filtering to predict user preferences and generate item suggestions based on historical rating patterns. The library includes dedicated tools for hyperparameter optimization and model evaluation. It allows for searching through parameter sets to find the most effective configurations and utilizes a suite of metrics to measure prediction accuracy. The framework covers the full development workflow, including data loading from various sources, the c

This is a PyTorch recommendation framework and deep learning recommendation model designed to generate personalized content predictions. It functions as a distributed embedding trainer that processes dense and sparse features through a neural network architecture to predict user preferences. The project implements a CUDA-optimized machine learning system using specialized GPU kernels to accelerate embedding lookup and aggregation. It employs a distributed approach to shard massive sparse feature tables across multiple GPUs, enabling the training of large-scale models. The system utilizes a t

Bustag is a containerized media library manager and automated metadata aggregator. It serves as a centralized database for tracking digital assets, utilizing machine learning models to act as a predictive content filter that identifies and recommends media based on user preferences. The system distinguishes itself through a supervised preference modeling workflow, where users manually label assets as liked or disliked to train predictive models. These models then automate content curation and personalized recommendations by analyzing labeled datasets. The platform integrates web data aggrega

Implicit is a Python recommendation engine and matrix factorization library designed for collaborative filtering. It implements predictive models that analyze implicit feedback to estimate user preferences and generate personalized item recommendations without requiring explicit ratings. The library utilizes native-code execution and multi-core parallelized processing to decompose large interaction matrices into latent factors. It incorporates approximate nearest neighbor indexing to accelerate high-dimensional similarity lookups and reduce recommendation latency. The framework covers prefer

The algorithm is a distributed recommendation engine pipeline designed to construct and serve personalized content timelines. It functions as a multi-stage orchestration layer that aggregates candidate content from diverse social graphs and high-dimensional embedding spaces, processing user interaction data to deliver a unified, ranked experience. The system utilizes a high-performance machine learning serving infrastructure to execute deep learning models that predict engagement probabilities in real-time. It distinguishes itself through a hybrid retrieval strategy that combines graph-traver

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

LanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector search engine. It serves as a high-performance backend for indexing and retrieving high-dimensional embeddings, providing the foundation for machine learning data pipelines. The system distinguishes itself through a combination of cloud-native object storage and immutable version tracking, allowing for data time-travel and reproducible AI experiments. It integrates hybrid search capabilities, merging dense vector similarity with BM25 full-text search and SQL-like scalar filters

This project is a collection of educational resources and instructional guides for learning deep learning and neural network implementation using TensorFlow. It provides a structured set of tutorials and notebooks written in Chinese, covering supervised and unsupervised learning tasks. The material focuses on practical implementations of diverse neural network architectures, including convolutional, recurrent, and autoencoder networks. It includes specific training content for computer vision, natural language processing, and generative models. The coverage extends to specialized network arc

Recommendable is a Ruby library designed to integrate recommendation engines directly into database-backed applications. It provides a framework for tracking user interactions, such as likes, dislikes, and bookmarks, to build detailed interest profiles and generate personalized content suggestions. The engine distinguishes itself by utilizing collaborative filtering to identify relationships between items based on overlapping user behavior. It supports both personalized suggestions tailored to individual preferences and aggregate popularity rankings that surface trending content across the en

PaddleRec is a deep learning recommendation library and distributed model training framework based on the PaddlePaddle framework. It provides a suite of industrial-scale algorithms and models for user matching and personalized content ranking. The project includes a recommendation inference engine for exporting and serving trained models to production environments for real-time online requests. It enables the implementation of deep learning recommendation algorithms for processing massive behavioral datasets. The framework covers large-scale model training across distributed computing cluste

Monolith is a distributed recommendation model framework and asynchronous training engine designed to build and train large-scale deep learning architectures. It functions as a distributed model trainer that processes massive datasets across multiple compute nodes using asynchronous update mechanisms. The system features a dedicated embedding table manager that creates unique, feature-isolated tables to prevent representation collisions. It also includes a real-time weight updater to capture immediate changes in user interest and data hotspots through continuous parameter synchronization. Th

This project is a recommendation system framework designed for building, evaluating, and operationalizing personalized item suggestion engines. It provides a comprehensive toolkit for implementing collaborative filtering and content-based algorithms, supported by an end-to-end machine learning pipeline for preparing datasets and deploying predictive models. The framework distinguishes itself through the integration of knowledge graphs to provide richer context for recommendations and the use of industry-specific patterns to accelerate system deployment. It also includes a specialized model ev

This project is a collection of educational Jupyter Notebooks providing tutorials on neural network construction and tensor operations using the TensorFlow framework. It serves as a machine learning educational repository and implementation guide for deep learning students. The suite focuses on specific advanced architectures, including convolutional networks for image classification, residual networks with skip connections for training stability, and variational autoencoders for generative modeling and data synthesis. It also includes guides for building denoising and deep autoencoders to pe

The algorithm-ml is a machine learning ranking engine designed to personalize content feeds by calculating relevance scores for items based on user interests and historical interaction data. It functions as a recommendation system that processes user behavior and item metadata to determine the optimal order of content for individual users. The system utilizes a multi-stage ranking architecture that filters large pools of candidate items into smaller sets before applying computationally expensive scoring models. It employs gradient-boosted decision tree ensembles to capture non-linear relation

This project serves as a comprehensive educational resource and technical guide for mastering deep learning through the PyTorch framework. It provides structured tutorials and practical code examples designed to teach core machine learning principles, ranging from fundamental tensor operations to the construction of complex neural network architectures. The repository distinguishes itself by bridging the gap between theoretical concepts and hands-on implementation. It covers the development of generative applications, such as image synthesis and style transfer, while offering guidance on opti

This project is an automated machine learning framework and toolkit designed for training and tuning custom models for classification, regression, and recommendations. It functions as a multimodal machine learning toolkit capable of processing and training models using a combination of text, image, audio, and sensor data. The framework distinguishes itself as a multimodal data processor that can handle and visualize large datasets on a single machine using column-oriented disk storage. It includes a core machine learning model generator that converts trained models into formats compatible wit

This project is a curated collection of technical reference materials and study guides designed for machine learning interview preparation. It provides comprehensive resources for candidates pursuing engineering roles, focusing on deep learning, production infrastructure, and large-scale system design. The repository distinguishes itself through an architecture that combines theoretical research with industrial case studies. It utilizes a pattern-based approach to system design, breaking down complex deployments—such as recommendation engines, search ranking, and ad click prediction—into reus

Leon is a framework for building personal AI assistants that integrates large language models with local tool execution and persistent memory. It functions as an agentic workflow orchestrator and modular skill engine, enabling the creation of autonomous assistants capable of planning and executing multi-step tasks. The system features a retrieval-augmented generation memory architecture that indexes conversation history and user facts for context-aware grounding. It utilizes a modular skill system to interact with external binaries and APIs, supported by a loop that handles tool calling, sche

This project is a structured educational curriculum designed to teach the fundamentals of building and training deep learning models. It provides a comprehensive guide for implementing neural networks using high-level machine learning frameworks and the Python programming language, focusing on practical, hands-on exercises for beginners. The tutorial distinguishes itself by covering the full lifecycle of model development, from initial construction to production-ready optimization. It includes specific modules on refining model performance through weight quantization and addressing data bias

Neural collaborative filtering is a recommendation system framework that predicts user item preferences from implicit feedback by combining generalized matrix factorization and multi-layer perceptron networks through a shared final embedding layer. It captures both linear and non-linear interactions to model user preferences from historical data. The framework executes training and evaluation runs through a configuration-driven pipeline accessible via command-line interfaces, parsing hyperparameters such as learning rates, batch sizes, and latent dimensions. It optimizes implicit feedback mod

This repository is a collection of implementation references and solved notebooks covering supervised, unsupervised, and reinforcement learning techniques. It provides practical guides for building predictive models, clustering algorithms, and autonomous agents. The project includes specific implementations for neural network architectures, such as multi-layer perceptrons for digit recognition, and recommender systems using collaborative and content-based filtering. It also features reinforcement learning systems that utilize deep Q-learning to optimize decision-making policies. The codebase

This repository provides a collection of interactive Jupyter notebooks designed to bridge theoretical machine learning concepts with practical implementation. It serves as a structured educational curriculum for deep learning, offering hands-on tutorials that guide users through the fundamentals of neural network architectures and their application. The project distinguishes itself by demonstrating identical neural network architectures across multiple industry-standard machine learning libraries, allowing for direct comparison and framework-agnostic learning. It includes utilities to transfo

This project is a TensorFlow learning course consisting of a deep learning tutorial series and guided modules. It provides the source code and documentation necessary to build and train neural network architectures and machine learning algorithms. The repository serves as a machine learning deployment guide, providing practical examples for moving trained models from development environments into production. It includes templates and guided tutorials for model development and prototyping. The course covers AI model education through a structured curriculum focused on tensor-based computation