Research & Education

Project-walkthroughs is a GitHub repository by Dataquestio that provides comprehensive project code for data science, machine learning, and web development. It includes files, Jupyter notebooks, and datasets designed to accompany live project walkthroughs available on the Dataquest YouTube channel. The resource is ideal for individuals looking to build complete, end-to-end projects to enhance their professional portfolios. Users should have a foundational understanding of Python, Pandas, NumPy, data cleaning, and machine learning basics to effectively utilize the projects. The repository covers a wide range of topics, from beginner machine learning to more advanced concepts like neural networks and web scraping.

The Center for Digital Health and Artificial Intelligence at Johns Hopkins (CDHAI) is a research initiative focused on exploring the convergence of digital technologies, artificial intelligence, and healthcare. CDHAI's primary objective is to conduct rigorous academic research that generates actionable insights into how these technological innovations can effectively address critical challenges within the healthcare sector. This includes improving healthcare quality, empowering patients through digital tools, and advancing health equity. The center's work contributes to the broader understanding of AI's potential and implications in medical and public health contexts, fostering advancements that benefit both practitioners and patients.

PyABSA is a modular and reproducible open-source framework designed for Aspect-based Sentiment Analysis (ABSA), bridging the gap from research to production. It offers a unified API for training, evaluation, and inference across multiple ABSA subtasks, including Aspect Polarity Classification (APC), Aspect Term Extraction & Polarity Classification (ATEPC), Aspect Sentiment Triplet Extraction (ASTE), and Aspect Category Opinion Sentiment Triplet Extraction (ASQP/ACOS). The framework comes with a Model Zoo of available checkpoints that auto-download, visualization tools for evaluation metrics, and helpers for dataset annotation. Additionally, PyABSA supports text augmentation for classification and adversarial defense, along with automatic device selection for CPU/GPU. It is ideal for researchers and developers working with sentiment analysis and natural language processing tasks.

recommendation is an Open Source workshop resource designed for individuals interested in building recommendation systems using both machine learning and deep learning. The resource delves into the theoretical underpinnings, including ML & DL formulation, prediction vs. ranking, and similarity metrics. It explores different paradigms such such as content-based, collaborative filtering, knowledge-based, hybrid, and ensemble approaches. Users can learn to work with various data types, including tabular, images, and text, and implement models like Matrix Factorization, Auto-Encoders, Wide & Deep, and Sequence Modelling. The workshop also covers practical aspects like setup, encoding, design, training, and evaluation, providing a comprehensive guide for developing and deploying recommendation systems.

AI Commons is a non-profit initiative dedicated to leveraging Artificial Intelligence as a common good to benefit humanity. It strives to build an equitable, accessible, ethical, and decentralized collaboration framework for AI-based problem-solving. The platform aims to engage a broad diversity of actors, including AI practitioners, entrepreneurs, academia, NGOs, and industry players, to focus on a wider range of solutions that respond to diverse global needs. By fostering a common voice, AI Commons seeks to address the world's challenges and ensure that the promise of AI benefits everyone. It serves as a hub for community and partners to contribute to making AI an integral part of everyone's future life.

Resin is a reboot of an older search engine project, now featuring a more sane architecture. It functions as a vector space search engine, a vector database, and a key/value store, designed for efficient string processing, vector operations, and custom storage primitives. The tool can produce large language models from strings and large 'anything' models from byte arrays. Key features include fast key/value storage with page/column readers and writers, practical text analysis utilities for various data types, and command-line tools for building and validating lexicons. Its design is clean, dependency-light, and easy to extend, making it suitable for developers working with search and machine learning applications.

reasoning-from-scratch is the official code repository for the book *Build a Reasoning Model (From Scratch)*, offering a hands-on approach to understanding and implementing reasoning large language models (LLMs) in PyTorch. Users start with a pre-trained base LLM and progressively add reasoning capabilities, mirroring approaches used in large-scale models like DeepSeek R1 and GPT-5 Thinking. The repository includes code for generating text, evaluating reasoning models, improving reasoning with inference-time scaling and self-refinement, and training models with reinforcement learning. It also covers distilling reasoning models for efficiency and provides bonus materials on topics like GPU optimization, advanced evaluation methods, and building chat interfaces. The code is designed to run on consumer hardware, with GPU utilization if available, making it accessible for a wide audience.

ReID-Survey is an open-source GitHub repository dedicated to deep learning for person re-identification. It offers comprehensive surveys, including an in-depth analysis of Transformer's impact across various Re-ID directions and a survey on deep learning for person re-identification with a powerful AGW baseline. The repository provides implementations for unsupervised Re-ID, cross-modality visible-infrared unsupervised Re-ID, and a unified experimental standard for animal Re-ID. Researchers can find code, datasets, and detailed experimental results for various Re-ID tasks, making it a valuable resource for advancing research in this field.

Quantitative-Research-Projects is a curated GitHub repository offering a collection of quantitative finance research projects. The projects delve into various aspects of financial analysis, including sector rotation, multi-factor models, and advanced AI-driven strategies utilizing machine learning and deep learning techniques. These strategies are applied across high, mid, and low frequencies, providing a comprehensive view of quantitative finance. Each project within the repository is accompanied by full code and detailed analysis, making it a valuable resource for researchers and practitioners. The collection is continuously updated, ensuring access to the latest research and methodologies in the field.

StrongSuit, previously known as Callidus Legal AI, is a comprehensive legal AI platform designed to enhance and speed up essential legal tasks for lawyers. It provides advanced AI legal research capabilities, including immediate answers to legal questions, analysis of complex fact patterns, and the ability to draft extensive memos and briefs. The platform also excels in contract redlining, allowing users to redline contracts significantly faster, summarize differences, compare against market standards, and generate AI-powered redline suggestions. Furthermore, StrongSuit assists with discovery and timelines, enabling the creation of timelines and statements of facts from relevant files, conducting document reviews, and improving writing. It aims to reduce hallucinations in legal research and offers a unified solution for various legal software needs.

robustlearn is an open-source library developed by Microsoft for research in robust machine learning, focusing on responsible AI. It offers a unified platform for exploring various aspects of robustness, including adversarial and backdoor attack and defense mechanisms, out-of-distribution (OOD) generalization, and safe transfer learning. The library hosts several projects like SpecFormer for adversarial robustness in Vision Transformers, NMtune for understanding label noise in pre-training, and RiFT for improving generalization of adversarial training. It also includes projects addressing OOD generalization for time series classification, domain-specific risk minimization, and activity recognition. robustlearn is designed to be extensible, allowing researchers to develop and test their own robust machine learning models.

RAG Time is a comprehensive 5-week learning journey designed to help users master Retrieval-Augmented Generation (RAG). Developed by Microsoft experts, this resource provides step-by-step guides, live coding samples, and expert insights to enable the creation of smarter AI applications. The program covers fundamental RAG concepts, building ultimate retrieval systems, optimizing vector indexes for scale, handling multimodal data, and exploring hero use cases, including Agentic RAG. It features exclusive video content, practical demonstrations, and sample code to facilitate hands-on learning, making complex concepts accessible through engaging visuals.

smolGPT offers a minimal PyTorch implementation for training small Large Language Models (LLMs) from scratch, designed primarily for educational purposes and simplicity. It boasts a pure PyTorch codebase with no abstraction overhead, incorporating modern architectural elements like Flash Attention (when available), RMSNorm, SwiGLU, and optional Rotary embeddings (RoPE). The tool supports efficient training features including mixed precision (bfloat16/float16), gradient accumulation, learning rate decay with warmup, weight decay, and gradient clipping. It also includes built-in TinyStories dataset processing and SentencePiece tokenizer training integration, making it a comprehensive yet accessible platform for learning LLM development.

Ethical Intelligence offers comprehensive AI literacy training and education designed for both individuals and organizations. The platform provides a range of learning opportunities including online courses, interactive workshops, and tailored custom programs. Its primary goal is to guide users from a state of confusion to confidence in navigating the complexities of artificial intelligence, fostering the necessary fluency to utilize AI wisely and responsibly. Ethical Intelligence aims to elevate the human element in the equation, ensuring that AI serves humanity effectively and ethically. The platform also emphasizes community, suggesting a collaborative environment for learning and discussion around AI ethics.

DaltonTx redefines drug discovery by providing an AI-enabled platform that serves as an intelligence backbone for modern R&D. It offers an adaptive intelligent system that evolves with scientific advancements, integrates seamlessly into existing workflows, and empowers users with lasting capabilities. The platform learns from every scientist, model, and experiment, continuously improving and guiding better decisions. DaltonTx's technology covers the full discovery lifecycle, including data ingestion, model training, molecule generation, and experiment prioritization. It is built by scientists for scientists, combining software engineering, machine learning, and deep drug discovery expertise to tackle complex problems in both small molecules and biologics.

Search-R1 is an open-source reinforcement learning framework designed for training large language models (LLMs) to effectively reason and make tool calls, specifically to search engines, in a coordinated manner. Built upon the veRL framework, it extends the concepts of DeepSeek-R1(-Zero) by integrating interleaved search engine access and offering a comprehensive RL training pipeline. This framework serves as an alternative to OpenAI DeepResearch, fostering research and development in tool-augmented LLM reasoning. It supports various RL methods like PPO, GRPO, and reinforce, accommodates different LLMs such as Llama3 and Qwen2.5, and integrates with diverse search engines including local sparse/dense retrievers and online search engines like Google and Bing.

SPO (Self-Supervised Prompt Optimization) is an AI tool hosted on Hugging Face Spaces designed to enhance the performance of language models by optimizing user prompts. It allows users to create or select templates, configure various settings, and initiate an optimization process to achieve better responses from AI models. This application is particularly useful for prompt engineers and researchers looking to fine-tune their interactions with large language models, ensuring more accurate and relevant outputs through a self-supervised learning approach. The tool aims to streamline the prompt engineering workflow, making it easier to experiment with and improve prompt effectiveness.

Semantra is a multipurpose command-line tool designed for semantic search across local documents, including text and PDF files. Unlike traditional keyword matching, Semantra allows users to query by meaning, providing a more intuitive and powerful search experience. It processes documents locally, launching a web search application for interactive querying. This tool is particularly useful for individuals needing to sift through large volumes of information, such as journalists analyzing leaked documents, researchers exploring academic papers, or students engaging with literature. Semantra prioritizes privacy and security by performing all analysis on the user's computer, and it offers configurable options for embedding models and search parameters.

File AI is an AI-native data preparation and automation platform designed to unify data capture, governance, and orchestration into auditable AI workflows. It transforms unstructured data into trusted intelligence across various enterprise functions. The platform features fileForge, an AI-native data intelligence engine, alongside purpose-built solutions like fileLedger for financial operations automation and fileShield for intelligent case management in regulated environments. Key capabilities include multimodal AI OCR, classification, schema extraction, SOP-driven workflow engines, and over 100 ERP and system integrations. File AI aims to build the foundation for agentic AI at scale, providing the context, validation, and control needed for AI agents to act with confidence in real enterprise workflows.

Farmdar is an agritech company that leverages AI and satellite technology to provide comprehensive crop insights to agribusinesses globally. The platform offers solutions like CropScan for crop classification, YieldPro for yield analytics, eSurvey for land surveying, and DeveloPro for development insights. By covering over 500 million acres, Farmdar empowers businesses in sectors like sugar mills, seed and fertilizer companies, and lending institutions to make data-driven decisions, enhance productivity, and promote sustainable agricultural practices. Its technology helps identify crop location, acreage, variety, yield, harvesting time, and potential disease or pest attacks, alongside health, stress, nitrogen, soil organic matter, and moisture levels.

Collate is a privacy-first AI reader designed for Mac users, enabling them to chat with, summarize, and extract insights from PDF documents entirely offline. This local-first approach ensures that all processing runs directly on your device, guaranteeing complete privacy as your documents never leave your computer. It supports both Apple Silicon (M1, M2, M3) and Intel Macs running macOS 13.1 or later. Users can ask questions, get instant summaries, and receive citation-backed answers with automatic highlighting. Collate also supports multi-PDF chat for comparative research, folder organization, and the ability to export summaries and conversations in various formats like PDF, rich text, or email. It's completely free to download and use, with no subscription fees or usage limits.

SwinIR is an official PyTorch implementation of the Swin Transformer model for image restoration. It excels in tasks such as classical, lightweight, and real-world image super-resolution, grayscale and color image denoising, and JPEG compression artifact reduction. The tool's deep feature extraction module, composed of residual Swin Transformer blocks, allows it to outperform state-of-the-art methods while potentially reducing the number of parameters. SwinIR provides interactive online demos, including a Colab demo for real-world image SR and a PlayTorch demo for mobile applications, making it accessible for both research and practical applications.

StyleGAN-Human Interpolation is a web-based tool hosted on Hugging Face Spaces, designed for generating and manipulating human faces using AI. It leverages StyleGAN models to create realistic synthetic faces, offering users the ability to explore the capabilities of this advanced generative adversarial network. The primary function of the tool is to produce a series of images that smoothly transition between two distinct, randomly generated human images. Users can control this interpolation process by adjusting parameters such as seed values and truncation psi, which influence the randomness and realism of the generated faces. This makes it a valuable resource for researchers, artists, and enthusiasts interested in AI-driven image synthesis and the nuances of facial generation.

Digital Punk offers comprehensive consulting services to organizations aiming to integrate artificial intelligence into their operations. The company provides strategic guidance, knowledge transfer, and practical actions to enhance efficiency, quality, and competitiveness. Key offerings include the 1-2-3-AI program for gradual AI adoption, specialized training plans for soft skills, management, and interpersonal relations, and marketing expertise to boost brand awareness and market positioning. Digital Punk also assists with strategic development and implementation, offering an holistic approach across marketing, human resources, and operations. Additionally, it fosters community engagement through the Digital Club, organizing events for knowledge sharing and networking.