Research & Education

Tbilisi AI Meetups serves as a community platform dedicated to organizing and promoting gatherings centered around Artificial Intelligence, Machine Learning, and Data Science. The initiative's core mission is to facilitate knowledge exchange and networking opportunities for professionals and enthusiasts within the local AI community. By hosting regular meetups, it provides a space for individuals to discuss emerging trends, share insights, and collaborate on projects, thereby strengthening the regional AI ecosystem. The platform is designed to be accessible to anyone interested in these fields, from beginners to experienced practitioners, fostering a vibrant and collaborative environment.

PageLM is a community-driven, open-source AI education platform designed to convert study materials into engaging and interactive learning experiences. Inspired by NotebookLM, it leverages state-of-the-art LLMs and TTS systems to create quizzes, flashcards, structured notes, and even podcasts from uploaded documents like PDFs, DOCX, Markdown, and TXT files. The platform offers features such as contextual chat for document interaction, automatic Cornell-style note generation, interactive quizzes with hints and scoring, and AI-powered podcast creation for on-the-go learning. It also includes a homework planner, exam simulator, AI debate partner, and a personalized study companion. PageLM supports multiple AI models including Google Gemini, OpenAI GPT, and Anthropic Claude, and is built with a modern tech stack including Node.js, React, and Docker.

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.

Women Who Do Data (W2D2) is a non-profit organization focused on empowering diversity within the artificial intelligence and data science fields. As a member-led initiative, W2D2 aims to advance women and underrepresented professionals in data, machine learning, and AI. The organization provides crucial opportunities for individuals to succeed in these rapidly evolving fields, promoting equitable progress and economic empowerment. By championing diverse talent, W2D2 strives to foster the creation of more inclusive and effective AI solutions, contributing to a more representative and innovative tech landscape.

The Udemy-prompt-engineering-course GitHub repository serves as a comprehensive companion for the Udemy Prompt Engineering Course. It is built around Jupyter notebooks, small example applications, prompt files, datasets, screenshots, and diagrams, offering a hands-on approach to learning prompt engineering. The repository covers a wide array of topics including OpenAI API workflows, advanced prompting techniques, retrieval, embeddings, RAG, agent design and orchestration, LangChain, LangGraph, evaluation, vision, and image generation. It is particularly strong in coding-heavy sections, providing practical examples for developers and teams looking for reference notebooks for LLM application development.

The TensorFlow-Machine-Learning-Cookbook is a comprehensive code repository published by Packt, designed to accompany the TensorFlow Machine Learning Cookbook. It offers all the necessary project files to work through the book, enabling users to gain practical experience with TensorFlow. The resource covers fundamental concepts such as variables, matrices, and data sources, progressing to advanced topics like Linear Regression, neural networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Natural Language Processing (NLP). Each chapter's code is organized into folders, making it easy to follow along. It is compatible with Python 3 and requires libraries like TensorFlow, Numpy, Scikit-Learn, Requests, and Jupyter, running on Mac, Windows, and Linux without special hardware. This repository is ideal for those looking to deepen their understanding and application of Google's machine learning library.

Omniscient AI Learning is a platform dedicated to providing structured, self-directed courses in artificial intelligence and machine learning. It distinguishes itself by offering integrated subjects, such as AI + Physics, catering to learners who prioritize clarity and focused learning experiences. The platform aims to cut through the clutter often found in online education, allowing users to build a strong foundation in complex topics without the need for fixed syllabi or endless, unstructured videos. It's designed for individuals looking for a clear and concise path to mastering AI and related fields.

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.

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.

Inclusive Growth Chain (IGC) is a technology company focused on driving impact through advanced technologies such as Blockchain, Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT). The company specializes in implementing blockchain-enabled services across critical sectors including financial inclusion, healthcare, women empowerment, education, and environmental sciences. IGC has a proven track record, highlighted by its success in winning international blockchain hackathons, demonstrating its expertise and innovative approach in applying these technologies for social good. While specific features are not detailed on the current website, its core mission revolves around leveraging cutting-edge tech to address societal challenges and foster inclusive growth.

LearnPrompt offers a comprehensive and permanently free open-source curriculum focused on AIGC (AI-Generated Content) technologies. The platform provides in-depth courses covering essential topics such as Prompt Engineering, ChatGPT, Midjourney, Runway, and Stable Diffusion. Beyond core generative AI, it expands into specialized areas like AI digital humans, AI voice and music generation, and the fine-tuning of large language models. With its latest v4.0 update, LearnPrompt features a new UI, multi-language support, a comments section, daily updates, and contribution options, making it a dynamic resource for anyone looking to master AIGC without cost. The platform is continuously updated with new content and features, including case studies and tutorials for advanced applications.

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.

Strikingo is an AI-powered education platform designed to transform learning by offering personalized experiences for both educators and learners. The platform leverages artificial intelligence to adapt to individual learning styles and paces, aiming to save educators valuable time in content creation and lesson planning. It focuses on delivering measurable results, ensuring that learning outcomes are tracked and improved. Strikingo supports a smart learning environment, making education more efficient and effective for a global audience. Its core mission is to enhance educational processes through intelligent, adaptive technology.

ML-Tutorial-Experiment is an open-source GitHub repository dedicated to providing comprehensive coding tutorials for machine learning. It aims to help users learn to code machine learning models through practical examples and experiments. The resource covers a wide array of topics, including building convolutional neural networks with TensorFlow, understanding and implementing Generative Adversarial Networks (GANs), exploring CapsNet architecture, and delving into RNNs and CNNs for sequence modeling. It also features tutorials on Transformer-based neural machine translation and foundational concepts like linear algebra, probability, Python basics, and NumPy. The project emphasizes reproducible code and aims to curate high-quality, error-free articles for developers and researchers.

Non-overwhelming-machine-learning is an open-source project hosted on GitHub, offering a carefully curated list of machine learning resources specifically designed for beginners. The primary goal is to provide a "non-overwhelming" introduction to the field, guiding users through a chronological learning path. It assumes foundational knowledge in probability, multivariable calculus, and optimization, ensuring that learners have the necessary prerequisites before diving into more complex topics. The resource list includes introductory courses like "Intro to Machine Learning UD120," "Deep Learning @ Udacity," and specialized courses on convolutional neural networks and natural language processing. This structured approach helps beginners build a solid understanding without feeling overwhelmed by the vastness of machine learning.

TensorFlow-and-DeepLearning-Tutorial is an open-source repository offering a collection of deep learning tutorials. Originally taught as an online course in 2016, it provides foundational knowledge in TensorFlow, fully connected neural networks, and convolutional neural networks. The resource also delves into Natural Language Processing concepts. Written primarily in Python and Jupyter Notebook, it serves as a valuable educational tool for individuals looking to understand and implement deep learning techniques.

Deep-learning-drizzle is an open-source GitHub repository offering a comprehensive collection of educational resources for various AI domains. It curates lectures and materials covering Deep Learning, Reinforcement Learning, Machine Learning Fundamentals, Natural Language Processing, Computer Vision, Optimization for Machine Learning, Automatic Speech Recognition, Bayesian Deep Learning, Medical Imaging, and Graph Neural Networks. The repository is designed to help individuals drench themselves in these subjects by providing links to courses from renowned universities and instructors like Geoffrey Hinton, Andrew Ng, and Stanford University. It serves as a valuable learning hub for students and researchers looking to deepen their understanding and develop intuitions in the rapidly evolving field of artificial intelligence.

CodePRO LK is a technology-driven platform dedicated to empowering individuals and businesses through innovative services and cutting-edge education. In today's AI-driven world, the platform offers a comprehensive suite of AI-powered services tailored to specific needs, from automating routine tasks to making data-driven decisions. Additionally, its educational resources provide in-depth insights into the latest AI advancements, enabling users to acquire the skills necessary to thrive in the digital age. Services include AI/ML solutions, software development, algorithmic design, and data analytics & insights. The platform also provides an ultimate roadmap to kickstart a journey in AI and Machine Learning, with content available in Sinhala Medium.

llm-universe offers a comprehensive tutorial for beginner developers interested in large language model (LLM) application development. The project is designed to be highly practical, guiding users through the creation of a personal knowledge base assistant on an Alibaba Cloud server. It covers essential topics such as LLM introductions, API calling methods for various models (including GPT, Baidu Wenxin, iFlytek Spark, and Zhipu AI), knowledge base construction, and building RAG (Retrieval Augmented Generation) applications. The tutorial emphasizes hands-on learning, simplifying complex concepts and focusing on core skills needed to develop LLM-powered applications, making it accessible even for those without a strong AI or algorithm background.

Handy-Multi-Agent is a comprehensive tutorial designed for developers interested in understanding and implementing multi-agent systems. Based on the CAMEL-AI framework, this guide starts with basic Agent development and progresses to complex Multi Agent applications. It emphasizes practical application and hands-on building, combining necessary theoretical knowledge with real-world examples. The project includes detailed documentation in the 'docs' directory and executable code in the 'code' directory, allowing users to run examples directly. It covers topics such as RAG, Memory, and Multi Agent techniques, aiming to enhance skills in building and managing intelligent agents and applying them to solve practical problems.

The McGill Artificial Intelligence Society (MAIS) is a student-run organization dedicated to making artificial intelligence more accessible to students. They achieve this by hosting a variety of initiatives, including bootcamps like MAIS 202 for ML fundamentals, workshops on applied machine learning topics, and Canada's largest AI hackathon, MAIS Hacks. MAIS also fosters community through events like the Learnathon, an undergraduate AI research conference, and the McGill AI Podcast, which connects ML principles to research disciplines. The society aims to connect McGill students with the broader Montreal AI ecosystem through industry events and networking opportunities.

stable-diffusion-tutorial provides a complete set of tutorials for Stable Diffusion, meticulously crafted over three months. This resource guides users from initial setup and configuration to advanced techniques like ControlNet and Lora model training. It covers essential topics such as installing Stable Diffusion, understanding model types, performing text-to-image and image-to-image generation, and installing extensions. Additionally, the tutorial includes sections on AI painting websites, high-definition image upscaling, and integrating AI painting plugins with Photoshop, making it a valuable resource for anyone looking to master Stable Diffusion.

Studiolo is an innovative AI tool designed to facilitate rapid and personalized learning experiences. Users can select any topic, from 'Korean Cooking Fundamentals' to 'Intro to Machine Learning,' and the platform aims for them to remember it within 45 minutes. It allows for the augmentation of learning inquiries with local assets, enabling a truly customized educational path. Studiolo also offers options to define expertise levels, current depth of understanding, and session lengths, ranging from 5 to 45 minutes. For those unsure what to learn, a chat feature helps guide the user, making it accessible for a wide range of learners seeking efficient knowledge acquisition.

awesome-chatgpt-zh is a comprehensive, open-source Chinese guide designed to empower users with the knowledge and resources to effectively leverage ChatGPT. Hosted on GitHub, this project offers detailed instructions, prompt engineering guidelines, and application development insights. It curates a wide array of free and paid ChatGPT resources, along with a list of top open-source projects and productivity tools built on ChatGPT's capabilities. The guide covers various aspects, from understanding what ChatGPT is to advanced topics like LLM development, RAG guidance, and AGI concepts, aiming to significantly boost user productivity.