zillionare

This repository contains a collection of articles and tutorials on quantitative finance, including topics such as machine learning, statistical arbitrage, and risk management. The articles are written in a clear and concise style, and they are suitable for both beginners and experienced practitioners. The repository also includes a number of Jupyter notebooks that demonstrate how to use Python for quantitative finance.

A drill of scientific methods, processes, algorithms, and systems to build stories & models. An in-depth learning resource for humans. This repository is designed for individuals aiming to excel in the field of Data and AI, providing video sessions and text content for learning. It caters to those in leadership positions, professionals, and students, emphasizing the need for dedicated effort to achieve excellence in the tech field. The content covers various topics with a focus on practical application.

llm_related is a repository that documents issues encountered and solutions found during the application of large models. It serves as a knowledge base for troubleshooting and problem-solving in the context of working with complex models in various applications.

Lector is a text analysis tool that helps users extract insights from unstructured text data. It provides functionalities such as sentiment analysis, keyword extraction, entity recognition, and text summarization. With Lector, users can easily analyze large volumes of text data to uncover patterns, trends, and valuable information. The tool is designed to be user-friendly and efficient, making it suitable for both beginners and experienced users in the field of natural language processing and text mining.

This repository provides baselines for various competitions, a few top solutions for some competitions, and independent deep learning projects. Baselines serve as entry guides for competitions, suitable for beginners to make their first submission. Top solutions are more complex and refined versions of baselines, with limited quantity but enhanced quality. The repository is maintained by a single author, yunsuxiaozi, offering code improvements and annotations for better understanding. Users can support the repository by learning from it and providing feedback.

Large Language Models (LLM) are language models built by deep neural networks containing hundreds of billions of weights, trained on a large amount of unlabeled text using self-supervised learning methods. Since 2018, companies and research institutions including Google, OpenAI, Meta, Baidu, and Huawei have released various models such as BERT, GPT, etc., which have performed well in almost all natural language processing tasks. Starting in 2021, large models have shown explosive growth, especially after the release of ChatGPT in November 2022, attracting worldwide attention. Users can interact with systems using natural language to achieve various tasks from understanding to generation, including question answering, classification, summarization, translation, and chat. Large language models demonstrate powerful knowledge of the world and understanding of language. This repository introduces the basic theory of large language models including language models, distributed model training, and reinforcement learning, and uses the Deepspeed-Chat framework as an example to introduce the implementation of large language models and ChatGPT-like systems.

SciML.ai is an open source software organization dedicated to unifying packages for scientific machine learning. It focuses on developing modular scientific simulation support software, including differential equation solvers, inverse problems methodologies, and automated model discovery. The organization aims to provide a diverse set of tools with a common interface, creating a modular, easily-extendable, and highly performant ecosystem for scientific simulations. The website serves as a platform to showcase SciML organization's packages and share news within the ecosystem. Pull requests are encouraged for contributions.

GenAiGuidebook is a comprehensive resource for individuals looking to begin their journey in GenAI. It serves as a detailed guide providing insights, tips, and information on various aspects of GenAI technology. The guidebook covers a wide range of topics, including introductory concepts, practical applications, and best practices in the field of GenAI. Whether you are a beginner or an experienced professional, this resource aims to enhance your understanding and proficiency in GenAI.

Emerging Trajectories is an open source library for tracking and saving forecasts of political, economic, and social events. It provides a way to organize and store forecasts, as well as track their accuracy over time. This can be useful for researchers, analysts, and anyone else who wants to keep track of their predictions.

GrowthHacking-Notes is a repository containing detailed notes, strategies, and resources related to growth hacking. It provides valuable insights and tips for individuals and businesses looking to accelerate their growth through innovative marketing techniques and data-driven strategies. The repository covers various topics such as user acquisition, retention, conversion optimization, and more, making it a comprehensive resource for anyone interested in growth hacking.

Enterprise h2oGPTe - GenAI RAG is a repository containing code examples, notebooks, and benchmarks for the enterprise version of h2oGPTe, a powerful AI tool for generating text based on the RAG (Retrieval-Augmented Generation) architecture. The repository provides resources for leveraging h2oGPTe in enterprise settings, including implementation guides, performance evaluations, and best practices. Users can explore various applications of h2oGPTe in natural language processing tasks, such as text generation, content creation, and conversational AI.

This repository contains material related to the new book _Synthetic Data and Generative AI_ by the author, including code for NoGAN, DeepResampling, and NoGAN_Hellinger. NoGAN is a tabular data synthesizer that outperforms GenAI methods in terms of speed and results, utilizing state-of-the-art quality metrics. DeepResampling is a fast NoGAN based on resampling and Bayesian Models with hyperparameter auto-tuning. NoGAN_Hellinger combines NoGAN and DeepResampling with the Hellinger model evaluation metric.

This repository contains a collection of resources for learning about and using Large Language Models (LLMs). The resources include tutorials, code examples, and links to additional resources. LLMs are a type of artificial intelligence that can understand and generate human-like text. They have a wide range of potential applications, including natural language processing, machine translation, and chatbot development.

This repository contains a collection of AI projects implemented in Python, primarily in Jupyter notebooks. The projects cover various aspects of artificial intelligence, including machine learning, deep learning, natural language processing, computer vision, and more. Each project is designed to showcase different AI techniques and algorithms, providing a hands-on learning experience for users interested in exploring the field of artificial intelligence.

AppFlowy.IO is an open-source alternative to Notion, providing users with control over their data and customizations. It aims to offer functionality, data security, and cross-platform native experience to individuals, as well as building blocks and collaboration infra services to enterprises and hackers. The tool is built with Flutter and Rust, supporting multiple platforms and emphasizing long-term maintainability. AppFlowy prioritizes data privacy, reliable native experience, and community-driven extensibility, aiming to democratize the creation of complex workplace management tools.

The multimodal cognitive AI repository focuses on research work related to multimodal cognitive artificial intelligence. It explores the integration of multiple modes of data such as text, images, and audio to enhance AI systems' cognitive capabilities. The repository likely contains code, datasets, and research papers related to multimodal AI applications, including natural language processing, computer vision, and audio processing. Researchers and developers interested in advancing AI systems' understanding of multimodal data can find valuable resources and insights in this repository.

Financial Datasets is an open-source Python library that allows users to create question and answer financial datasets using Large Language Models (LLMs). With this library, users can easily generate realistic financial datasets from 10-K, 10-Q, PDF, and other financial texts. The library provides three main methods for generating datasets: from any text, from a 10-K filing, or from a PDF URL. Financial Datasets can be used for a variety of tasks, including financial analysis, research, and education.

This repository contains a collection of articles and tutorials on quantitative finance, including topics such as machine learning, statistical arbitrage, and risk management. The articles are written in a clear and concise style, and they are suitable for both beginners and experienced practitioners. The repository also includes a number of Jupyter notebooks that demonstrate how to use Python for quantitative finance.

finagg is a Python package that provides implementations of popular and free financial APIs, tools for aggregating historical data from those APIs into SQL databases, and tools for transforming aggregated data into features useful for analysis and AI/ML. It offers documentation, installation instructions, and basic usage examples for exploring various financial APIs and features. Users can install recommended datasets from 3rd party APIs into a local SQL database, access Bureau of Economic Analysis (BEA) data, Federal Reserve Economic Data (FRED), Securities and Exchange Commission (SEC) filings, and more. The package also allows users to explore raw data features, install refined data features, and perform refined aggregations of raw data. Configuration options for API keys, user agents, and data locations are provided, along with information on dependencies and related projects.

AData is a free and open-source A-share database that focuses on transaction-related data. It provides comprehensive data on stocks, including basic information, market data, and sentiment analysis. AData is designed to be easy to use and integrate with other applications, making it a valuable tool for quantitative trading and AI training.

Jupyter Quant is a dockerized environment tailored for quantitative research, equipped with essential tools like statsmodels, pymc, arch, py_vollib, zipline-reloaded, PyPortfolioOpt, numpy, pandas, sci-py, scikit-learn, yellowbricks, shap, optuna, ib_insync, Cython, Numba, bottleneck, numexpr, jedi language server, jupyterlab-lsp, black, isort, and more. It does not include conda/mamba and relies on pip for package installation. The image is optimized for size, includes common command line utilities, supports apt cache, and allows for the installation of additional packages. It is designed for ephemeral containers, ensuring data persistence, and offers volumes for data, configuration, and notebooks. Common tasks include setting up the server, managing configurations, setting passwords, listing installed packages, passing parameters to jupyter-lab, running commands in the container, building wheels outside the container, installing dotfiles and SSH keys, and creating SSH tunnels.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.