Research & Education

BiHilo Academy offers specialized training and career support for science graduates aiming to enter the medical and pharmaceutical sectors. The program includes 12 practical modules covering sales, marketing, regulatory affairs, quality control, and more, designed to equip students with industry-specific language and skills. Participants receive individual tutoring, career coaching, and assistance with CV optimization and LinkedIn profiles. BiHilo also provides real-world insights from professionals working in leading medical companies and offers support until employment, including exclusive job offers and interview feedback. The curriculum is structured to help graduates identify suitable roles and gain the competencies needed for entry-level positions with competitive salaries.

Machine-learning-learning-notes is an open-source GitHub repository dedicated to providing comprehensive study notes for Zhou Zhihua's influential 'Machine Learning' book, often referred to as the 'Watermelon Book'. This resource meticulously details different types of machine learning algorithms, including supervised, unsupervised, semi-supervised, reinforcement learning, ensemble methods, dimensionality reduction, and feature selection. The notes are designed to clarify complex concepts, offering insights and expanded knowledge points to help new learners grasp the material effectively. It serves as a valuable companion for anyone studying machine learning, particularly those working through the 'Watermelon Book', and includes references to other classic works like Li Hang's 'Statistical Learning'.

Andrew-Ng-Deep-Learning-notes is an open-source GitHub repository offering detailed notes and practice code for Andrew Ng's renowned Deep Learning course. The notes are primarily in Chinese, making it a valuable resource for Chinese-speaking learners. The repository includes completed assignments, allowing users to review and understand practical applications of the course material. While it doesn't guarantee to cover every aspect of the course, it serves as an excellent supplementary learning tool. Users are encouraged to contribute by raising issues or submitting pull requests for any identified inaccuracies, fostering a collaborative learning environment.

python-Machine-learning is an open-source GitHub repository dedicated to machine learning algorithms and projects. It serves as a valuable resource for individuals looking to gain practical experience in machine learning, offering a collection of code examples from various projects and competitions. The repository is maintained by Mryangkaitong and encourages contributions from the community. It also provides links to related blogs and a WeChat official account for in-depth explanations and updates on the projects, making it a comprehensive learning hub for machine learning enthusiasts and practitioners.

Creax is an innovation agency with over 25 years of experience, partnering with global innovators to identify future playing fields and address complex challenges. The agency offers services across three key areas: Visionary Roadmaps to sharpen innovation journeys, Smart Opportunities to uncover new markets and applications through emerging trends, and Sustainable Solutions to rethink products and processes with data-driven insights. Creax emphasizes a unique methodology that combines continuous data analysis with creative exploration, helping clients develop next-gen solutions, improve innovation processes, reduce risk, and accelerate progress. They have successfully completed over 1,250 projects across diverse industries.

3D-GRAND is a Densely-Grounded 3D-LLM designed to bridge the gap between natural language descriptions and 3D environments. Users can select a 3D scene and input a query to describe specific objects or locations. The tool then provides visual highlights of the relevant objects directly overlaid on the 3D model, offering a unique way to interact with and understand 3D data through text. This AI tool facilitates research in 3D understanding and grounding, making complex 3D scenes more accessible and interpretable through language-based interaction. It is available as a Hugging Face Space, indicating its potential for academic and research-oriented applications.

3DOI is a research tool hosted on Hugging Face Spaces, designed for the academic exploration of 3D object interaction. The project focuses on understanding how 3D objects behave and interact when presented with only a single image as input. This tool is primarily intended for researchers and academics in the field of computer vision and artificial intelligence who are working on problems related to 3D reconstruction, scene understanding, and object manipulation from limited visual data. While the current live website indicates a runtime error preventing full functionality, the underlying goal is to provide a platform for experimentation and development in this specialized area.

ClearSKY Vision offers cloudless Sentinel-2 satellite imagery, leveraging AI for cloud removal and data fusion. It integrates optical and SAR data from Sentinel-1 and Sentinel-2 satellites to reconstruct cloud-covered areas, clean optical pixels, and provide harmonized, analysis-ready images. This tool delivers frequent, consistent data at 10m resolution, available in Cloud Optimized GeoTIFF (COG) format, with options for TOA or BOA products. It supports flexible ordering via GeoJSON, WKT, or tiles, catering to agriculture, forestry, and environmental monitoring, ensuring uninterrupted insights even under persistent cloud cover.

RewardBench is an open-source benchmark and evaluation tool specifically designed for assessing the capabilities and safety of reward models, including those utilizing Direct Preference Optimization (DPO). The repository offers common inference code compatible with various reward models such as Starling, PairRM, OpenAssistant, and DPO. It ensures fair evaluation through standardized dataset formatting and testing procedures. Additionally, RewardBench includes robust analysis and visualization tools to help researchers and developers interpret results effectively. It supports quick evaluation of any reward model on any preference set, with features for logging model outputs and accuracy scores, and options for generative models (LLM-as-judge) and DPO models. The platform also facilitates contributing models to a public leaderboard and offers offline ensemble testing.

Conlan is a dynamic team of experienced developers specializing in innovative mobile applications powered by artificial intelligence. Their product EveryScan transforms smartphones into sophisticated visual encyclopedias, using state-of-the-art AI image recognition for identifying animals, insects, plants, coins, and culinary items. Another key offering is the AI Skin Scanner, an innovative mobile application that utilizes cutting-edge AI technology to analyze skin and provide precise insights into its health and condition. Conlan is committed to excellence, creativity, and user-centric design, continuously upgrading their skills to stay at the forefront of AI advancements in the mobile app landscape.

MuseO is an iOS mobile application designed to democratize music creation. It enables users to generate original music, beats, and AI covers simply by providing text or lyrics. The app aims to simplify the music production process, allowing individuals to create studio-quality tracks across a wide range of genres without requiring prior musical experience or instruments. Beyond ringtones, MuseO also offers the capability to create custom music for personal photos and videos, making it a versatile tool for content creators and music enthusiasts alike. Its intuitive interface focuses on ease of use, making advanced music generation accessible to everyone.

schnetpack is an open-source toolbox designed for researchers and developers working with atomistic systems. It provides a robust framework for developing and applying deep neural networks to predict various properties of molecules and materials, such as potential energy surfaces and quantum-chemical characteristics. The tool includes fundamental building blocks for atomistic neural networks, simplifying the process of conducting simulations and making accurate property predictions. Its open-source nature, hosted on GitHub, encourages community contributions and provides transparent access to its codebase, making it a valuable resource for academic and industrial research in computational chemistry and materials science.

Satellite-Imagery-Datasets-Containing-Ships is a comprehensive GitHub repository that curates radar and optical satellite datasets specifically designed for ship detection, classification, semantic segmentation, and instance segmentation tasks. These datasets are invaluable for researchers and developers working in computer vision, machine learning, remote sensing, and maritime analysis. The repository details various datasets, including SSDD, OpenSARship, SAR-Ship-Dataset, AIR-SARShip, HRSID, LS-SSDD, and FUSAR-Ship, providing information on their authors, year, tasks supported, and direct access links. Each dataset entry includes specifics like image dimensions, spatial resolutions, polarization types, and annotation formats, making it a crucial resource for developing and evaluating algorithms for maritime surveillance and naval operations.

TextClassification-Keras is a comprehensive code repository designed for implementing deep learning models for text classification tasks using the Keras framework. It offers ready-to-use implementations of popular models such as FastText, TextCNN, and TextRNN, making it a valuable resource for researchers and developers. The repository simplifies the application of these advanced models to text classification problems, supporting both English and Chinese documents. It serves as an excellent starting point for those looking to explore or integrate deep learning-based text classification into their projects, providing a foundational codebase for further development and experimentation.

The 'techniques' GitHub repository serves as a comprehensive resource for deep learning methods specifically tailored for satellite and aerial imagery analysis. It provides an organized overview of various techniques designed to handle the unique challenges of processing large-scale image datasets. The repository focuses on methodologies for identifying diverse object classes within these images, making it a valuable asset for researchers and developers in the field. As an open-source project, it is freely accessible for both research and development purposes, fostering collaboration and advancement in the application of AI to geospatial data.

TFC-pretraining is a specialized tool designed for self-supervised contrastive learning, specifically tailored for time series data. It leverages a novel approach called time-frequency consistency to significantly improve the learning process and the quality of representations derived from complex time series. The tool provides researchers and practitioners with not only the underlying methodology but also includes processed datasets and readily available code for implementing the technique. This makes it an invaluable resource for those working in time series analysis, enabling them to explore advanced predictive analytics and pattern recognition with greater efficiency and accuracy. Its focus on robust representation learning addresses key challenges in handling sequential data.

Teach Catalyst AI is an AI-powered teaching assistant designed to help educators streamline their work, nurture passion, and combat burnout. Developed by the creator of Classroom Management Expert, this platform offers a comprehensive suite of tools for various teaching needs. Key features include a Schedule Generator, Classroom Assessment Advisor, Math Problem Generator, Quiz Generator, and Curriculum Creator AI. It also provides tools for fostering positive teacher-student relationships, managing classroom distractions, and generating lesson plans, teaching instructions, and student reports. The platform aims to make teaching materials creation faster and more efficient, allowing teachers to personalize content without limitations and save significant preparation time. It emphasizes ease of use, requiring no technical expertise, and supports teachers in enhancing their career development and classroom management.

Awesome-seml is a comprehensive, curated list of articles dedicated to software engineering best practices for developing machine learning applications. This resource goes beyond core ML algorithms, focusing instead on the crucial surrounding activities such as data ingestion, coding standards, rigorous testing, version control, seamless deployment, quality assurance, and effective team collaboration. It serves as an invaluable guide for ML engineers and software engineers aiming to build robust, reliable, and production-ready machine learning systems. The list is categorized into broad overviews, data management, model training, deployment and operation, social aspects, governance, and tooling, offering a structured approach to understanding and implementing best practices.

awesome-gpt4 is an open-source GitHub repository offering a comprehensive, curated list of resources centered around the GPT-4 language model. It serves as a valuable hub for researchers, developers, and enthusiasts looking to delve deeper into GPT-4's applications and advancements. The repository categorizes resources into several key areas, including impactful scientific papers, a diverse collection of open-source projects leveraging GPT-4, community-contributed demos showcasing its capabilities, and various product integrations that utilize the model. Additionally, it features a section dedicated to GPT-4 news and announcements, keeping users updated on the latest developments. A significant part of awesome-gpt4 is its collection of impressive prompts, demonstrating effective ways to interact with GPT-4 for various tasks, from acting as a pharmacologist or lawyer to a debugger or mobile app developer. This makes it an indispensable resource for understanding, experimenting with, and developing applications based on GPT-4.

Brian2 is a free, open-source simulator for spiking neural networks, primarily written in Python. It provides a user-friendly and efficient platform for researchers to model and simulate complex neural circuits. The simulator is designed with ease of learning and use in mind, aiming to save scientists' time in addition to processing power. Brian2 is highly flexible and easily extensible, making it suitable for a wide range of neuroscience research applications. It is available on almost all platforms and offers comprehensive documentation. Users are encouraged to report issues via GitHub or the Brian forum and to cite the provided article if used for published research.

Python Institute is a certification portal dedicated to providing globally recognized Python certifications. It offers a structured pathway from entry-level (PICEP) to architect-level (PICA) certificates, validating Python expertise across various career stages. The institute provides comprehensive study materials and proctored exams, ensuring a rigorous and trusted certification process. Professionals can register, purchase certificates, schedule exams, and claim lifetime valid certifications and badges. With over 18,000 professionals certified in 41+ countries, Python Institute aims to empower individuals with career-accelerating Python skills recognized by employers worldwide.

Whisper AI Voice to Text is an iOS mobile application designed to provide fast and accurate transcription and dictation services. Users can effortlessly convert spoken audio into written text, making it ideal for transcribing meetings, lectures, or personal notes. Beyond basic transcription, the app offers advanced features such as summarizing lengthy audio content, allowing users to quickly grasp key information without listening to the entire recording. It also supports instant translation of foreign audio, breaking down language barriers. A unique interactive chat feature enables users to engage with their transcripts, asking questions and extracting specific details, enhancing productivity and information retrieval.

VLMEvalKit is an open-source evaluation toolkit designed for large vision-language models (LVLMs), supporting over 220 LMMs and 80+ benchmarks. It simplifies the evaluation process by allowing one-command evaluation without extensive data preparation across multiple repositories. The toolkit uses generation-based evaluation for all LVLMs, offering results with both exact matching and LLM-based answer extraction. Recent updates include improved handling for models with thinking mode and long responses, as well as multi-node distributed inference support for faster evaluations. It aims to provide an easy-to-use, reproducible evaluation environment for researchers and developers.

PlagiarismCheck.org is an accurate online plagiarism and AI content detector designed for teachers, students, writers, and businesses. It helps users assess the originality of any text, verify authorship, and trace AI-generated content with high accuracy. The tool employs advanced algorithms to detect even the slightest similarities, including substitutions, rearrangements, and hidden plagiarism, across various databases. It integrates seamlessly with educational platforms like Canvas, Moodle, Google Classroom, Schoology, Brightspace, and Populi, and offers a Google Docs Add-on. Beyond plagiarism detection, it includes a grammar checker, citation generator, and essay grader to improve writing quality. Users can check 2 pages for free, and the service emphasizes strict confidentiality and downloadable reports.