Search Results for "tutorials" - Page 4
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Showing 162 open source projects for "tutorials"
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Generative AI for beginners with JS
Join a time-traveling adventure where you meet history’s legends
Generative AI with JavaScript is an educational repository created by Microsoft that teaches developers how to build applications powered by large language models using the JavaScript ecosystem. The project is structured as a multi-lesson curriculum that introduces the concepts, tools, and practical techniques required to create generative AI applications. Each lesson includes written explanations, hands-on exercises, quizzes, and supporting videos to help developers learn the material... -
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The LLM Evaluation guidebook
Sharing both practical insights and theoretical knowledge about LLM
The Evaluation Guidebook is an open educational resource created by Hugging Face that explains how to evaluate machine learning and large language models effectively. It compiles practical insights and theoretical knowledge gathered from real-world evaluation work, including experience managing the Open LLM Leaderboard and designing evaluation tools. The guidebook teaches developers how to design evaluation pipelines, select appropriate metrics, and interpret model performance results. It... -
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AI CODE GUIDE
AI Code Guide is a roadmap to start coding with AI
AI CODE GUIDE is an open educational repository that provides a comprehensive roadmap for learning how to use artificial intelligence tools for software development. The project organizes resources related to AI-assisted programming, including tutorials, research papers, developer tools, and practical guidelines. Its goal is to help both experienced programmers and beginners understand how AI models can assist with writing, reviewing, and designing software. The repository discusses emerging practices such as AI-assisted coding workflows, agent-driven development, and “vibe coding,” where developers collaborate with AI systems to generate software. ... -
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AI Agents From Scratch
Demystify AI agents by building them yourself. Local LLMs
AI Agents from Scratch is an educational repository designed to teach developers how to build autonomous AI agents using large language models and modern AI frameworks. The project walks through the process of constructing agents step by step, beginning with simple prompt-based interactions and gradually introducing more advanced capabilities such as planning, tool use, and memory. The repository provides example implementations that demonstrate how language models can interact with external... -
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llms-from-scratch-cn
Build a large language model from 0 only with Python foundation
llms-from-scratch-cn is an educational open-source project designed to teach developers how to build large language models step by step using practical code and conceptual explanations. The repository provides a hands-on learning path that begins with the fundamentals of natural language processing and gradually progresses toward implementing full GPT-style architectures from the ground up. Rather than focusing on using pre-trained models through APIs, the project emphasizes understanding... -
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Mistral Finetune
Memory-efficient and performant finetuning of Mistral's models
mistral-finetune is an official lightweight codebase designed for memory-efficient and performant finetuning of Mistral’s open models (e.g. 7B, instruct variants). It builds on techniques like LoRA (Low-Rank Adaptation) to allow customizing models without full parameter updates, which reduces GPU memory footprint and training cost. The repo includes utilities for data preprocessing (e.g. reformat_data.py), validation scripts, and example YAML configs for training variants like 7B base or... -
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mlpack
mlpack: a scalable C++ machine learning library
...Aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. mlpack uses CMake as a build system and allows several flexible build configuration options. You can consult any of the CMake tutorials for further documentation, but this tutorial should be enough to get mlpack built and installed. -
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Codel
Fully autonomous AI Agent that can perform complicated tasks
Fully autonomous AI Agent that can perform complicated tasks and projects using terminal, browser, and editor. -
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E2B Cookbook
Examples of using E2B
E2B Cookbook is an open-source collection of example projects, guides, and reference implementations demonstrating how to build applications using the E2B platform. The repository acts as a practical learning resource for developers who want to integrate AI agents with secure cloud execution environments that allow large language models to run code and interact with tools. The examples illustrate how developers can build AI workflows capable of performing tasks such as data analysis, code... -
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Prompt in-context learning
Resources for in-context learning and prompt engineering
Prompt-In-Context-Learning is an open-source repository that serves as a comprehensive engineering guide and curated resource collection for understanding and applying in-context learning and prompt engineering with large language models. The project gathers research papers, tutorials, prompt examples, and practical guides that help developers and researchers learn how to design effective prompts for models such as GPT-3, ChatGPT, and other foundation models. In-context learning refers to the ability of language models to learn a task directly from examples provided in the prompt without updating the model’s parameters, allowing them to perform new tasks through demonstration alone. ... -
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handy-ollama
Implement CPU from scratch and play with large model deployments
handy-ollama is an open-source educational project designed to help developers and AI enthusiasts learn how to deploy and run large language models locally using the Ollama platform. The repository serves as a structured tutorial that explains how to install, configure, and use Ollama to run modern language models on personal hardware without requiring advanced infrastructure. A key focus of the project is enabling users to run large models even without GPUs by leveraging optimized CPU-based... -
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Gemma
Gemma open-weight LLM library, from Google DeepMind
...It includes APIs for conversational sampling, parameter management, and integration with fine-tuning methods like LoRA. The Gemma library can operate efficiently on CPUs, GPUs, or TPUs, with recommended configurations depending on model size. Through included tutorials and Colab notebooks, users can explore examples covering sampling, multi-modal interactions, and fine-tuning workflows. By providing accessible open-weight models, Gemma enables researchers and developers to experiment with state-of-the-art LLM architectures. -
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PennyLane
A cross-platform Python library for differentiable programming
A cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network. Built-in automatic differentiation of quantum circuits, using the near-term quantum devices directly. You can combine multiple quantum devices with classical processing arbitrarily! Support for hybrid quantum and classical models, and compatible with existing machine learning libraries. Quantum circuits can be set up to interface with either NumPy,... -
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PyTorch Geometric Temporal
Spatiotemporal Signal Processing with Neural Machine Learning Models
The library consists of various dynamic and temporal geometric deep learning, embedding, and Spatio-temporal regression methods from a variety of published research papers. Moreover, it comes with an easy-to-use dataset loader, train-test splitter and temporal snaphot iterator for dynamic and temporal graphs. The framework naturally provides GPU support. It also comes with a number of benchmark datasets from the epidemiological forecasting, sharing economy, energy production and web traffic... -
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AIF360
A comprehensive set of fairness metrics for datasets
...AI Fairness 360 package is available in both Python and R. The AI Fairness 360 interactive experience provides a gentle introduction to the concepts and capabilities. The tutorials and other notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available. Being a comprehensive set of capabilities, it may be confusing to figure out which metrics and algorithms are most appropriate for a given use case. To help, we have created some guidance material that can be consulted. -
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scikit-learn-videos
Jupyter notebooks from the scikit-learn video series
...The project emphasizes accessibility and beginner-friendly explanations, making it suitable for learners who are new to data science or machine learning programming. The tutorials collectively span several hours of instructional content and demonstrate how to build predictive models using Python tools commonly used in the data science ecosystem. -
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TensorFlow Privacy
Library for training machine learning models with privacy for data
...This repository contains the source code for TensorFlow Privacy, a Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy. The library comes with tutorials and analysis tools for computing the privacy guarantees provided. -
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OpenCV
Open Source Computer Vision Library
...Languages: C++, Python, Julia, Javascript Homepage: https://opencv.org Q&A forum: https://forum.opencv.org/ Documentation: https://docs.opencv.org Source code: https://github.com/opencv Please pay special attention to our tutorials! https://docs.opencv.org/master Books about the OpenCV are described here: https://opencv.org/books.html -
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AnimateDiff
Plug-n-play module turning text-to-image models into animation
AnimateDiff is an open-source project designed to enhance text-to-image diffusion models by adding animation capabilities. It allows users to turn static images generated by popular text-to-image models into animated sequences without requiring additional model training. This plug-and-play tool is compatible with a wide range of community models and facilitates the generation of animation directly from pre-existing text-to-image models. It supports various configurations to create animations... -
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pattern_classification
A collection of tutorials and examples for solving machine learning
...The repository also covers algorithms such as Bayesian classification, logistic regression, neural networks, clustering methods, and ensemble models. In addition to algorithm tutorials, the project contains supplementary resources such as dataset collections, visualization examples, and links to recommended books and talks. These materials are designed to support both theoretical understanding and practical experimentation with machine learning tools. -
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AI System & AI Infra
Tutorial repository focused on the full-stack design of AI systems
AI System is an educational and technical tutorial repository focused on the full-stack design of artificial intelligence systems, covering the foundational infrastructure that powers modern deep learning workloads. The project explores the AI software and hardware stack end to end, including AI chips, AI compilers, inference engines, and training frameworks, helping learners understand how these components interact in real-world deployments. Rather than being a single library, it functions... -
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ReplitLM
Inference code and configs for the ReplitLM model family
...These models are designed specifically for coding workflows and are trained on large datasets of source code covering many programming languages and development environments. The repository also includes documentation and tutorials for integrating the models into development tools, APIs, or research pipelines. Developers can fine-tune the models using instruction-tuning techniques to adapt them for specific programming tasks or domains. The models were trained using modern deep learning techniques and large-scale GPU infrastructure to achieve strong performance in code completion and generation tasks. -
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LLM Cookbook
LLM Introduction Tutorial for Developers, Chinese version
LLM Cookbook is an open-source learning repository designed to help developers understand how to build applications powered by large language models through practical examples and translated course material. The project adapts and reproduces content from widely known LLM developer courses and reorganizes it into a structured learning path tailored for developers who want to build real AI applications. It covers the essential topics required to start working with LLM APIs and frameworks,... -
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EasyRL
Reinforcement learning (RL) tutorial series
easy-rl is a beginner-friendly reinforcement learning (RL) tutorial series and framework developed by Datawhale China. It provides educational resources and implementations of various RL algorithms to help new researchers and practitioners learn RL concepts. -
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AI Explainability 360
Interpretability and explainability of data and machine learning model
...The AI Explainability 360 interactive experience provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The tutorials and example notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available. There is no single approach to explainability that works best. There are many ways to explain: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, etc. It may therefore be confusing to figure out which algorithms are most appropriate for a given use case.
