Research & Education

deep-learning-with-python-notebooks is a GitHub repository containing Jupyter notebooks that implement the code samples from the book "Deep Learning with Python" by Francois Chollet and Matthew Watson. It includes notebooks for the third edition (2025), as well as legacy notebooks for the second (2021) and first (2017) editions. These notebooks focus on runnable code blocks and section titles, omitting text paragraphs, figures, and pseudocode, making them ideal for side-by-side use with the book. Users can run the code on Colab, which provides a hosted runtime with all necessary dependencies, or locally by setting up a Jupyter environment. The code for the third edition uses Keras 3 and supports JAX, TensorFlow, or PyTorch as backends. The repository also provides instructions for using Kaggle data and setting up Kaggle API keys as Colab secrets.

AI Song and Lyrics Generator is a mobile application designed to empower users to effortlessly create original song lyrics and musical ideas. The tool utilizes smart AI technology to generate complete songs, including melodies, harmonies, and vocals, based on user-selected moods, themes, or keywords. This innovative approach significantly simplifies the songwriting process for both beginners and experienced content creators, enabling quick musical composition without requiring prior musical experience. Users can easily export their creations as high-quality MP3s, making it simple to share or further develop their musical projects. The app aims to democratize music creation by making advanced AI capabilities accessible to a broad audience.

UADAMAGE is an AI and GIS company specializing in geospatial analytics for automatic damage monitoring. The platform leverages satellite and drone imagery alongside advanced computer vision to assess damage following war or natural disasters. It transforms these diverse data inputs into actionable insights, aiding governments, organizations, and partners in making data-driven decisions. UADAMAGE's core focus areas include infrastructure recovery, demining efforts, and environmental monitoring, providing critical information for post-disaster assessment and planning.

devops-ai-guidelines is a comprehensive resource designed to guide DevOps engineers through their AI journey, from initial AI tool usage to becoming an AI Infrastructure Architect. The repository offers structured learning paths, practical tips, and enterprise guidelines for implementing AI safely and effectively within teams and organizations. It covers a wide range of topics including building MCP servers with Golang and Kubernetes, creating AI agents with LangChain, and leveraging AI for AWS infrastructure management and project management. The resource also includes strategies for career acceleration, interview preparation, and daily productivity tips, making it a valuable asset for individuals and teams looking to integrate AI into their DevOps practices.

DeepReg is a freely available, community-supported open-source toolkit designed for research and education in medical image registration using deep learning. It is built on TensorFlow 2 for efficient training and rapid deployment of models. The toolkit implements major unsupervised and weakly-supervised algorithms, along with their combinations and variants, focusing on growing and diverse clinical applications. All DeepReg Demos utilize openly accessible data, and it offers simple built-in command-line tools that require minimal programming. DeepReg operates under the Apache 2.0 license, promoting an open, permissible, and research-and-education-driven environment.

deeplearning-papernotes is an open-source repository offering curated summaries and notes on a wide array of Deep Learning research papers. This resource is designed to help researchers, students, and engineers efficiently navigate the vast landscape of AI literature. It provides concise overviews, key insights, and sometimes links to associated articles and code, enabling users to quickly understand complex topics without having to read every full paper. The repository is organized by month and year, making it easy to track recent advancements and historical developments in the field. It serves as a valuable reference for staying updated on the latest breakthroughs and foundational concepts in deep learning.

Deeplearning-guide is a comprehensive and evolving resource designed to help individuals learn Deep Learning effectively. Inspired by Haseeb Qureshi's guide on Blockchain Development, it curates and shares valuable resources found throughout the author's own learning journey. The guide is structured into several phases, starting with prerequisites like coding and mathematics, moving through Deep Learning fundamentals, and encouraging practical application through project creation. It emphasizes a mix of theoretical understanding and hands-on coding, recommending MOOCs like deeplearning.ai and fast.ai, alongside complementary materials such as YouTube channels, blogs, and coding libraries like NumPy and Matplotlib. All recommended resources are free, making it accessible for anyone interested in diving deeper into Deep Learning.

deep_learning_object_detection is a comprehensive GitHub repository dedicated to cataloging research papers focused on object detection utilizing deep learning techniques. It serves as a valuable resource for academics and practitioners, offering an organized list of papers from 2014 onwards, including key publications from major conferences like CVPR, ICCV, and NIPS. The repository also provides links to both official and unofficial code implementations for many of the listed papers, facilitating replication and further research. Additionally, it includes performance tables comparing various detectors across different datasets like VOC and COCO, along with update logs detailing the continuous curation of new research.

Edvice is an AI-driven platform designed to empower students in their career exploration and decision-making process. It offers expert guidance and personalized assessments to help individuals discover their ideal career path. The platform assists students in making informed choices by providing tailored career paths, recommendations for skill development, and insights into both STEM and non-STEM fields. Beyond career guidance, Edvice also supports students with scholarship and admissions assistance, aiming to build a mission-driven community of certified career counselors.

DeepFAS serves as an official repository for "Deep Learning for Face Anti-Spoofing: A Survey," offering a comprehensive review of recent advancements in deep learning techniques for face anti-spoofing (FAS). The resource covers various methodologies, including hybrid, pure deep learning, and generalized learning approaches for monocular RGB FAS, as well as multi-modal and specialized sensor-based FAS. It meticulously details publicly available datasets, outlining their characteristics, setup, and attack types, alongside classical evaluation protocols. Researchers and developers can leverage this tool to understand the landscape of FAS, compare different methods, and identify suitable datasets for their work, making it an invaluable academic resource.

ModelingToolkit.jl is a high-performance symbolic-numeric computation framework designed for scientific computing and scientific machine learning within the Julia ecosystem. It allows users to define models at a high level, enabling symbolic preprocessing for analysis and enhancement. The tool can automatically generate optimized functions for model components, such as Jacobians and Hessians, and automatically sparsify and parallelize computations. It also applies automatic transformations, like index reduction, to simplify models for numerical solvers. ModelingToolkit.jl supports composing multiple ODE subsystems and simulating complex Differential-Algebraic Equations (DAEs), making it a powerful tool for advanced scientific modeling and simulation.

DeepLearningZeroToAll is an open-source project offering a comprehensive collection of TensorFlow basic tutorial labs. It provides practical code examples designed to help users understand fundamental deep learning concepts. While the current tutorials are primarily in Korean, there are plans to release English video tutorials, making it accessible to a broader audience. The project emphasizes readability and understandability over efficiency, making it an excellent resource for instructional purposes. It covers various deep learning topics, including linear regression, logistic regression, softmax classifiers, CNNs, and RNNs, with examples implemented in TensorFlow, Keras, MXNet, and PyTorch. The repository encourages community contributions and provides guidelines for code style and testing.

Decode Chess is an AI-powered chess analysis tool designed to explain the reasoning and concepts behind chess engine suggestions in intuitive language. It acts as a personal chess explainer, helping players with an ELO rating of up to 2000 understand complex positions and improve their game. The tool combines a unique AI algorithm with the Stockfish NNUE engine to provide in-depth analysis, including opponent threats, good moves, future plans, piece functionality, and relevant positional/tactical concepts. Users can upload games in PGN or FEN formats for analysis, play against an adaptive AI opponent, and receive personalized instructional feedback.

Spacum is an independent internet privacy company offering a free web browser and search engine designed to protect user data. Unlike traditional browsers and search engines, Spacum does not track searches or browsing history and actively blocks third-party trackers, ads, and cookie pop-ups. Its comprehensive privacy features include web tracking protection to stop Facebook and Google trackers, email protection to intercept and remove hidden email trackers, and app tracking protection for Android devices. Spacum aims to provide a secure and efficient browsing experience, making privacy accessible to everyone without compromising search relevance or speed. It is available across multiple platforms including Windows, macOS, Android, and iOS.

DR Tulu is an open-source Deep Research (DR) model designed for tackling long-form research tasks. The DR Tulu-8B model has demonstrated performance comparable to OpenAI DR on long-form DR benchmarks. This repository provides the official code for DR Tulu, including an agent library with a MCP-based tool backend, high-concurrency async request management, and a flexible prompting interface for developing and training deep research agents. It also includes RL training code based on Open-Instruct and SFT training code based on LLaMA-Factory, allowing for supervised fine-tuning and reinforcement learning with GRPO and evolving rubrics. An interactive CLI demo is available for users to experiment with DR Tulu-8B.

dissecting-reinforcement-learning is an open-source repository offering Python code, PDFs, and supplementary resources for a series of blog posts on Reinforcement Learning. It serves as a comprehensive guide for practitioners and students, covering fundamental concepts like Markov chains, Bellman Equation, Monte Carlo methods, and Temporal Difference Learning. The repository also delves into advanced topics such as Actor-Critic methods, Evolutionary Algorithms, and various function approximation techniques including neural networks. It provides standalone Python environments for classic RL problems like the Inverted Pendulum, Mountain Car, and Multi-Armed Bandit, which do not require external installations like OpenAI Gym. This makes it an accessible resource for hands-on learning and experimentation.

Halleluyah Healthcare leverages AI, powered by JadaAI, to offer comprehensive healthcare information, integrating traditional remedies with modern health suggestions. The platform functions as a digital health companion, providing holistic guidance and facilitating connections to essential healthcare services. It is specifically designed to extend valuable health resources to underserved populations, promoting better living through accessible and intelligent health insights. The tool focuses on empowering individuals with knowledge to manage their health proactively, combining technological innovation with the wisdom of holistic traditions for a well-rounded approach to wellness.

DLFS_code is a GitHub repository containing all the code from the book "Deep Learning From Scratch," published by O'Reilly in September 2019. It is designed for readers to clone and systematically step through the code to better understand the deep learning concepts presented in the book. The repository is structured by chapter, with each chapter featuring two notebooks: a Code notebook with runnable Python code and a Math notebook for LaTeX equations. It includes implementations of deep learning models, such as a single-layer CNN trained from scratch in pure Numpy to achieve over 90% accuracy on MNIST, as detailed in the book's Appendix.

EconML is a Python package developed by Microsoft Research as part of the ALICE (Automated Learning and Intelligence for Causation and Economics) project. It provides a toolkit for estimating heterogeneous treatment effects from observational data, integrating advanced machine learning techniques with econometrics. The package is designed to measure the causal effect of treatment variables on an outcome, controlling for various features, and how this effect varies. It supports methods like Double Machine Learning, Causal Forests, Orthogonal Random Forests, and Meta-Learners, offering flexibility in modeling effect heterogeneity while preserving causal interpretation and providing confidence intervals. EconML is built on standard Python packages for Machine Learning and Data Analysis, making it accessible for data scientists and researchers.

EntitySeg is an open-source toolbox designed for advanced image segmentation tasks, focusing on open-world and high-quality segmentation. It consolidates several cutting-edge algorithms developed by the qqlu group, including Open-World Entity Segmentation (TPAMI2022), High Quality Segmentation for Ultra High-resolution Images (CVPR2022), CA-SSL: Class-Agnostic Semi-Supervised Learning (ECCV2022), and High-Quality Entity Segmentation (ICCV2023 Oral). The toolbox is built using Python and PyTorch, making it accessible for researchers and developers in the computer vision domain. It aims to provide a unified platform for various image segmentation challenges, with future plans to merge all projects for enhanced interoperability and support.

dtu_mlops is an open-source repository designed for the Machine Learning Operations course at DTU, providing comprehensive exercises and supplementary materials. It aims to introduce students to essential coding practices for organizing, scaling, monitoring, and deploying machine learning models in both research and production settings. The repository offers hands-on experience with various frameworks, both local and cloud-based, for large-scale machine learning. Key learning objectives include efficient code organization, reproducibility through containerized applications, version control for collaboration, continuous integration and machine learning, debugging, profiling, visualization, and monitoring of experiments. It also covers using cloud computing services for scaling experiments, distributed training paradigms, and deploying models locally and in the cloud. The course material is freely available under the Apache 2.0 license.

The MT-Reading-List is a comprehensive resource for researchers and students interested in machine translation. Maintained by the Tsinghua Natural Language Processing Group, it offers a curated collection of papers spanning the evolution of the field, from statistical machine translation (SMT) to neural machine translation (NMT). While it prioritizes contemporary NMT papers, it also acknowledges historical context, referencing older papers. The list is continuously updated and categorized, covering various sub-topics like model architectures, attention mechanisms, low-resource translation, multilingual MT, robustness, interpretability, and efficiency. It also includes sections for "10 Must Reads" and WMT winners, making it a valuable starting point for anyone delving into machine translation research.

OpenDeepWiki is an open-source project, inspired by DeepWiki, designed to help developers understand and utilize code repositories more effectively. Built on .NET 9 and Semantic Kernel, it offers features like code analysis, documentation generation, and knowledge graph construction. The platform supports various code repositories including GitHub, GitLab, and Gitee, and can analyze all programming languages. Key capabilities include automatically generating Mermaid diagrams for code structure, supporting custom AI models, and providing AI-driven code analysis for deep understanding. It also generates SEO-friendly documentation using Next.js and allows conversational interaction with AI to retrieve detailed code information. The modular design ensures easy expansion and customization, making it a powerful tool for knowledge management and collaboration.

efficient-gnns is a comprehensive repository offering code and resources for developing scalable and efficient Graph Neural Networks (GNNs). It specifically focuses on knowledge distillation techniques, including novel approaches like Graph Contrastive Representation Distillation, to create resource-efficient GNNs. The repository benchmarks various distillation methods, such as Local Structure Preserving loss and Global Structure Preserving loss, alongside baselines like Logit-based KD. It supports research on large-scale, real-world graph datasets for tasks like graph classification on MOLHIV and node classification on ARXIV and MAG, providing installation and usage instructions for researchers and developers in the field.