Research & Education

LLM-eval-survey is the official GitHub page for the survey paper "A Survey on Evaluation of Large Language Models." It functions as a central repository for researchers and practitioners, offering a curated collection of papers and resources focused on the evaluation of large language models (LLMs). The repository organizes papers by various evaluation aspects, including natural language processing tasks (understanding, sentiment analysis, text classification, inference, reasoning, generation, summarization, dialogue, translation, question answering), robustness, ethics, biases, trustworthiness, and applications in social science, natural science, engineering, medical, and agent domains. It also provides updates on new paper versions and welcomes community contributions.

llm-paper-daily is an open-source GitHub repository dedicated to curating and updating a daily list of research papers focused on large language models (LLMs). This resource is invaluable for researchers, academics, and enthusiasts who need to stay abreast of the rapid advancements in the LLM field. The repository offers a structured way to discover new papers, often including summaries and direct links to the original sources, making it easier to navigate the vast amount of new research. By providing a centralized and frequently updated collection, llm-paper-daily significantly reduces the time and effort required to track the latest developments in AI research, particularly in areas like agents, RAG (Retrieval-Augmented Generation), and general LLM applications.

nlp_overview is an open-source project dedicated to providing an up-to-date learning resource on modern deep learning techniques in natural language processing (NLP). It delves into the theoretical foundations and implementation specifics of deep learning models such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and reinforcement learning. The resource covers a wide array of NLP tasks and applications, including machine translation, question answering, and dialogue systems, and summarizes state-of-the-art results. The project aims to be a collaborative and open resource, guiding researchers and enthusiasts through emerging concepts, benchmark datasets, and code releases in the NLP field.

The python-machine-learning-book-3rd-edition repository hosts the code examples for the third edition of the "Python Machine Learning" book. This resource is designed to complement the book, offering practical implementations of machine learning concepts using Python. It includes code for various chapters covering topics such as training algorithms, Scikit-Learn classifiers, data pre-processing, dimensionality reduction, model evaluation, ensemble learning, sentiment analysis, web application embedding, regression analysis, clustering, neural networks (from scratch and with TensorFlow), deep convolutional neural networks, recurrent neural networks, generative adversarial networks, and reinforcement learning. Users can explore these examples to deepen their understanding of the theoretical concepts presented in the book.

PyTorchNLPBook is a comprehensive companion repository for the book "Natural Language Processing with PyTorch," published by O'Reilly Media. It offers a rich collection of code and data designed to help users understand and implement NLP solutions using the PyTorch framework. The repository covers fundamental concepts such as PyTorch basics, foundational neural network components, and various NLP techniques including feed-forward networks, word embeddings, sequence modeling, and advanced topics like attention mechanisms and neural machine translation. It's an invaluable resource for anyone looking to learn and apply deep learning to natural language processing, providing hands-on examples and practical implementations directly from the book.

UniWorld is an open-source project from PKU-YuanGroup focused on advancing visual AI through high-resolution semantic encoders. It provides a unified framework for understanding, generation, and editing of visual content. The project includes UniWorld-OSP2.0 for VLM-enhanced image-to-video generation, UniWorld-V2 for image editing using diffusion models, and UniWorld-V1 for a broad range of visual tasks. All data, models, training code, and evaluation code are open-sourced, making it a valuable resource for AI researchers and computer vision engineers. The framework demonstrates excellent performance across various tasks, including subject consistency, background consistency, and aesthetic quality in video generation, and precise instruction execution in image editing.

Tensorflow-speech-recognition is an open-source project designed for speech recognition using Google's TensorFlow deep learning framework and sequence-to-sequence neural networks. It was developed as a replacement for caffe-speech-recognition. While the project is no longer actively maintained or up-to-date with the latest TensorFlow versions or state-of-the-art theory, it remains valuable for educational purposes. The repository provides various scripts for tasks like number classification, speaker classification, and speech-to-text, along with installation instructions for dependencies like pyaudio and portaudio. Users interested in modern speech recognition are advised to explore alternatives like Mozilla DeepSpeech or Whisper.

Beam ATG, also known as Beam, offers an advanced credit decisioning platform leveraging open finance methodologies and artificial intelligence. The tool is designed to streamline loan applications, clarify eligibility, and enhance the overall lending experience, particularly for African communities. Key features include the ability to increase conversion rates, boost loan volume, and significantly decrease administrative costs through automation and machine learning. Beam also focuses on mitigating bad debt risk with advanced algorithms and data analytics, promoting responsible lending. It provides powerful and easy-to-use APIs to unify consistent data and automate credit analysis, navigating the complex African credit landscape.

The OWASP Top 10 for Large Language Model Applications is a foundational resource for understanding and mitigating security risks in applications leveraging large language models. Housed under the OWASP GenAI Security Project, this initiative provides a broad consensus on the most critical security vulnerabilities specific to LLM technologies. It aims to offer practical, actionable, and concise security guidance for developers, data scientists, and security experts involved in designing and building LLM-powered applications and plugins. The project explores how conventional vulnerabilities manifest uniquely in LLM contexts and how traditional remediation strategies must adapt. It serves as a starting point for newcomers and a reference for experienced professionals in the evolving field of LLM application security.

The C3.ai Digital Transformation Institute is a research consortium focused on advancing the application of artificial intelligence across various sectors. Established in March 2020, the institute brings together leading scientists to conduct research and train practitioners in the Science of Digital Transformation. This interdisciplinary field operates at the intersection of AI, machine learning, cloud computing, internet of things, big data analytics, organizational behavior, public policy, and ethics. The consortium includes C3 AI, Microsoft Corporation, and several prominent universities and national laboratories, jointly managed by the University of California, Berkeley, and the University of Illinois Urbana-Champaign. It supports research through various programs, including colloquia, symposia, and workshops, and publishes newsletters and articles on emerging science and research findings.

The nlp-in-python-tutorial is an open-source guide designed to introduce users to Natural Language Processing (NLP) using Python. It leverages Jupyter Notebooks and popular data science libraries to provide a hands-on learning experience. The tutorial covers various NLP concepts through practical examples, including data cleaning, exploratory data analysis, sentiment analysis, topic modeling, and text generation. It is ideal for individuals new to NLP with Python, offering a structured approach to understanding and implementing NLP techniques. The tutorial was initially created in 2018, with an updated 2025 NLP course available for those seeking more current content.

DeepInsight Platform is an AI-powered research tool designed to significantly reduce the time spent on academic and professional research. It leverages artificial intelligence with human oversight to efficiently process and analyze vast amounts of information. The platform excels at reading, analyzing, and citing credible sources, ensuring the reliability of its outputs. Users can expect comprehensive reports and podcasts as deliverables, making complex research findings accessible and digestible. Unlike standard search engines, DeepInsight Platform aims to provide deep research capabilities, offering a more thorough and structured approach to information gathering and synthesis. This tool is ideal for anyone needing to conduct in-depth research quickly and accurately, with a focus on verifiable sources.

NLP-Projects is a comprehensive open-source repository dedicated to Natural Language Processing. It provides a wide array of concepts and practical scripts covering fundamental and advanced NLP topics. Users can explore implementations for word2vec, sentence2vec, machine reading comprehension, dialog systems, and text classification. The collection also delves into pretrained language models like XLNet, BERT, ELMo, and GPT, alongside sequence labeling, information retrieval, information extraction, knowledge graphs, text generation, and network embedding. It serves as a valuable resource for understanding and implementing various NLP techniques, with some sections offering Chinese notes for deeper insights.

AI_Curriculum is an open-source repository offering a comprehensive collection of Deep Learning and Reinforcement Learning lectures from leading universities such as Stanford, MIT, and UC Berkeley. This resource is designed to support students and educators in the field of artificial intelligence by providing access to high-quality, structured learning materials. The curriculum covers various topics including Applied Machine Learning, Introduction to Deep Learning, CNNs for Visual Recognition, NLP with Deep Learning, Unsupervised Learning, Multi-Task and Meta Learning, and Deep Reinforcement Learning. Each section typically includes links to lecture videos, course websites, and sometimes GitHub notebooks, making it a valuable hub for self-paced learning and academic reference.

Algorithm_Interview_Notes-Chinese is an open-source GitHub repository offering extensive interview notes for various technical roles, including algorithm, deep learning, and natural language processing (NLP). The resource is designed to assist candidates preparing for job interviews in 2018, 2019, and during spring/autumn recruitment seasons. It covers a wide array of topics such as machine learning, deep learning, C, C++, and Python, alongside general computer science knowledge relevant to algorithm positions. The repository also compiles questions from numerous machine learning and deep learning interview experiences, providing a practical study guide. It explicitly excludes topics related to frontend, testing, Java, or Android development.

Bilateral AI is an Austrian Cluster of Excellence dedicated to advancing artificial intelligence by integrating symbolic and sub-symbolic AI. This project aims to overcome the limitations of current narrow AI systems, which are typically focused on specific tasks like object or speech recognition. By combining symbolic AI's logical rules with sub-symbolic AI's (like ChatGPT) data-driven learning, Bilateral AI seeks to develop 'Broad AI' capable of diverse applications and human-like reasoning. The initiative emphasizes creating AI that is not only fast and expandable but also safe, trustworthy, and understandable for everyday use. It involves cutting-edge research modules focusing on reasoning, learning, adaptability, and efficiency, and actively seeks to foster the next generation of AI researchers.

daily-paper-computer-vision is an open-source GitHub repository dedicated to curating and organizing academic papers in the fields of computer vision, deep learning, and machine learning. The repository is updated daily, offering a timely resource for researchers and enthusiasts to stay abreast of the latest advancements. It compiles papers from major conferences and journals, including CVPR, NIPS, ICLR, ECCV, and AAAI, often providing links to both the papers and their associated codebases. This makes it an invaluable resource for academic research, literature reviews, and tracking developments in AI subfields such as object detection, semantic segmentation, Transformer models, and large language models.

Awesome-LLM-Learning is a comprehensive open-source repository designed to guide individuals through the intricacies of Large Language Models (LLMs). It offers foundational knowledge in deep learning and natural language processing, essential for understanding LLMs. The resource delves into core LLM concepts, including training frameworks like Megatron-lm and DeepSpeed, parameter-efficient fine-tuning (PEFT), classic open-source LLMs, RLHF, CoT/ToT, and SFT training. Additionally, it covers LLM inference techniques such as Huggingface parameters and KVCache, and explores applications like LangChain. The repository also features a section dedicated to cutting-edge research, recommending relevant papers and blogs to keep learners updated with the latest advancements in the field. It's an invaluable resource for both newcomers and experienced professionals looking to deepen their understanding and practical skills in LLM development.

Data-Science-EBooks is an open-source repository offering a comprehensive collection of high-quality ebooks focused on Data Science, Machine Learning, and Artificial Intelligence. This resource is designed to cater to both beginners looking to establish foundational knowledge and advanced learners seeking to deepen their expertise. The repository covers a wide array of topics including AI, Agentic AI, Basics of Data Science, Data Engineering & Pipeline, Data Science Cheatsheets, Deep Learning, Generative AI, MLOps, Machine Learning, Math for Data Science, NLP, Software Engineering, and System Design. It serves as an excellent resource for anyone looking to enhance their understanding and skills in these rapidly evolving fields.

Deep_and_Machine_Learning_Projects is an open-source GitHub repository containing a diverse collection of machine and deep learning projects. This resource provides readily available code and data files, enabling users to explore and implement practical applications of artificial intelligence. Each project within the repository is designed to be a standalone example, allowing individuals to understand specific use cases and integrate them into their own real-life scenarios. It serves as an excellent learning resource for those looking to gain hands-on experience in AI development, offering a practical approach to mastering machine and deep learning concepts through direct implementation.

FineTuningLLMs is the official repository for the book "A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face." This resource offers comprehensive guidance and practical code examples for fine-tuning large language models. It covers essential concepts such as quantization, low-rank adapters (LoRA), and dataset formatting templates. The repository features Jupyter notebooks that can be easily run on Google Colab with GPU support, making it accessible for hands-on learning. It delves into topics like loading quantized models, fine-tuning with SFTTrainer, and deploying models locally using formats like GGUF with Ollama or llama.cpp. The guide is designed for an intermediate-level audience, assuming a foundational understanding of deep learning concepts.

TheBloke Wizard Vicuna 13B Uncensored HF is an AI chatbot hosted as a Hugging Face Space. This tool offers an uncensored version of the Wizard Vicuna 13B model, allowing users to engage in conversational AI interactions without typical content restrictions. While the live website currently indicates a runtime error, suggesting it may not be fully operational at this moment, the intention is to provide a platform for direct interaction with this specific large language model. It is designed for those interested in exploring the capabilities of uncensored AI models within a readily accessible web environment.

Nested Knowledge, Inc. offers an AI-powered software platform designed to revolutionize systematic literature review and meta-analysis. The tool provides comprehensive capabilities for researchers, including advanced search functionalities, efficient screening processes, and robust data extraction tools. It also features powerful visualization insights to help users understand complex data more clearly. By automating and assisting in these critical research stages, Nested Knowledge aims to significantly accelerate the research workflow, enabling the creation of updatable syntheses of evidence and enhancing the overall efficiency and quality of academic research.

FlashPaper is an AI writing tool designed to support students and researchers in academic writing tasks. It offers features for generating graduation theses within ten minutes, creating outlines, and paraphrasing text. The tool also includes functionalities for plagiarism detection, text rewriting, and citation generation. FlashPaper aims to simplify the academic writing process by providing AI-powered assistance for various stages, from initial topic generation and literature review to final paper refinement and formatting. It supports tasks like generating opening reports and literature reviews, making it a comprehensive aid for academic work.