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Awesome AIGC Tutorials

Awesome License: MIT GitHub Repo stars

English | δΈ­ζ–‡η‰ˆ

Awesome AIGC Tutorials houses a curated collection of tutorials and resources spanning across Large Language Models, AI Painting, and related fields. Discover in-depth insights and knowledge catered for both beginners and advanced AI enthusiasts.

πŸ”” Recent Updates

🌱 How to Contribute

We warmly welcome contributions from everyone, whether you've found a typo, a bug, have a suggestion, or want to share a resource related to AIGC. For detailed guidelines on how to contribute, please see our CONTRIBUTING.md file.

πŸ“œ Content

πŸ‘‹ Introduction

  • AI for Everyone - Andrew Ng
    • "AI for Everyone" is a beginner's guide to understanding AI's practical applications, its limitations, and its societal impact, ideal for business professionals and leaders alike.
  • Practical AI for Teachers and Students - Wharton School
    • Wharton Interactive's crash course delves into the mechanics and impacts of LLMs, spotlighting models like OpenAI's ChatGPT4, Microsoft's Bing in Creative Mode, and Google's Bard.
  • Artificial Intelligence for Beginners - Microsoft
    • This 12-week Microsoft curriculum dives deep into AI methodologies, spanning symbolic AI to neural networks, while highlighting TensorFlow and PyTorch frameworks, yet omits business applications, classic machine learning, and certain cloud-specific topics.
  • Generative AI learning path - Google Cloud
    • This learning path offers a comprehensive journey from the basics of Large Language Models to deploying generative AI solutions on Google Cloud.

πŸ’¬ Large Language Models

πŸ’‘ Prompt Engineering

  • ChatGPT Prompt Engineering for Developers - DeepLearning.AI
    • Co-taught by OpenAI and DeepLearning.AI, this course guides learners in leveraging Large Language Models for tasks like summarizing and text transformation, with hands-on experiences in a Jupyter notebook environment.
  • Building Systems with the ChatGPT API - DeepLearning.AI
    • Led by experts from OpenAI and DeepLearning.AI, this course teaches automating workflows using language models, creating prompt chains, integrating Python, and designing chatbots, all through hands-on Jupyter notebook exercises with just basic Python knowledge required.
  • LangChain for LLM Application Development - DeepLearning.AI
    • Guided by the creator of LangChain and Andrew Ng, this course dives into advanced LLM techniques like chaining operations and using models as reasoning agents, empowering learners to craft robust applications quickly with foundational Python knowledge.
  • LangChain: Chat with Your Data - DeepLearning.AI
    • Delve into Retrieval Augmented Generation and chatbot creation based on document content with LangChain, covering data loading, splitting, embeddings, advanced retrieval techniques, and interactive chatbot building, designed for Python-savvy developers keen on harnessing Large Language Models.
  • Prompt Engineering for ChatGPT - Vanderbilt University
    • Unlock the potential of Large Language Models like ChatGPT by mastering prompt engineering, transitioning from basic to sophisticated prompts, enabling diverse applications ranging from writing to simulation, suitable for anyone with basic computer skills.
  • Prompt Engineering Guide - DAIR.AI
    • This guide introduces Prompt Engineering, a discipline that optimizes interactions with Large Language Models, offering extensive resources, research, and tools.
  • Learn Prompting
    • Dive into a beginner-friendly guide on Generative AI and Prompt Engineering, offering insights from industry giants, and explore how these tools revolutionize content creation and the future of work.
  • LangChain AI Handbook - James Briggs and Francisco Ingham
    • Explore the transformative world of LangChain, mastering core components, crafting effective prompts, and harnessing advanced AI agents, conversational memories, and custom tools for cutting-edge applications.

πŸ”§ LLMs in Practice

  • LLM Bootcamp - The Full Stack
    • Delve deep into prompt engineering, LLM operations, user experience design for language interfaces, augmented language model techniques, foundational LLM insights, hands-on projects, and the future of LLMs, complemented by expert talks from industry leaders on training and agent design.
  • Finetuning Large Language Models - DeepLearning.AI
    • Learn the techniques of finetuning large language models (LLMs) with Sharon Zhou, gaining expertise in data preparation, training, and updating neural net weights for improved results tailored to your data.
  • CS25: Transformers United V3 - Stanford University
    • This course delves into the transformative role of Transformers in deep learning, particularly their impact on the advancement of language models like ChatGPT and GPT-4.
  • Learn the fundamentals of generative AI for real-world applications - AWS x DeepLearning.AI
    • This course, in partnership with AWS, offers deep insights into generative AI and Large Language Models (LLMs). Participants will learn the mechanics, optimization, and real-world applications of LLMs from AWS AI experts. Suitable for professionals in AI and machine learning, with a Coursera certificate upon completion. Basic Python and machine learning knowledge recommended.

πŸ”¬ Theory of LLMs

🎨 AI Painting

πŸ§‘β€πŸŽ¨ Art Fundamentals and AI Painting Techniques

🌊 Stable Diffusion Principles and Applications

  • How Diffusion Models Work - DeepLearning.AI
    • Master generative AI in 'How Diffusion Models Work', an intermediate course by Sharon Zhou, where you'll craft diffusion models from scratch, enriched with hands-on coding and labs, ideal for those proficient in Python, Tensorflow, or Pytorch.
  • Hugging Face Diffusion Models Course
    • The Hugging Face course offers an in-depth look into diffusion models, guiding participants through media generation, hands-on training, and customization using the Diffusers library, with a foundational understanding of Python and Deep Learning essential for the best experience.
  • Practical Deep Learning for Coders part 2: Deep Learning Foundations to Stable Diffusion - fast.ai
    • This course offers an in-depth exploration of Stable Diffusion algorithms, covering advanced deep learning techniques and hands-on projects using PyTorch, empowering students with expertise in cutting-edge diffusion models.

πŸ”Š AI Audio

  • Hugging Face Audio Course
    • The Hugging Face Audio course teaches how to use transformers for various audio tasks, from speech recognition to generating speech from text, combining theory with hands-on exercises for learners familiar with deep learning.
  • CS224S: Spoken Language Processing - Stanford University
    • An immersive course on spoken language technology, covering dialog systems, deep learning in speech recognition and synthesis, with hands-on projects using modern tools like PyTorch, Alexa Skills Kit, and SpeechBrain, culminating in student-driven research or system design projects.

🌈 Multimodal

🧠 Deep Learning

  • Neural Networks/Deep Learning - StatQuest
    • Discover the intricacies of Neural Networks in this highly popular YouTube playlist, seamlessly blending informative graphics with expert teachings, captivating countless students from basics to advanced image classification with Convolutional Neural Networks.
  • Neural Networks - 3Blue1Brown
    • 3Blue1Brown unveils the magic of neural networks through vivid animations and clear explanations, diving deep into hand-written digit recognition, the nuances of gradient descent, and the intricate calculus behind backpropagation.
  • Neural Networks: Zero to Hero - Andrej Karpathy
    • Andrej Karpathy's course guides students from the foundational backpropagation to advanced neural networks like GPT, emphasizing language models as a versatile gateway to mastering deep learning, with prerequisites in Python programming and basic math.
  • Practical Deep Learning for Coders - fast.ai
    • Practical Deep Learning for Coders 2022 is a free course offering hands-on experience in building, training, and deploying deep learning models across various domains using tools like PyTorch and fastai, suitable for those with coding knowledge and without the need for advanced math.
  • Deep Learning Specialization - Andrew Ng
    • Andrew Ng's Deep Learning Specialization is a top-rated, self-paced program on Coursera with over 1 million learners, offering clear modules and practical techniques in AI, supported by a vast community and breaking down the latest in machine learning into understandable content.
  • 6.S191: Introduction to Deep Learning - Massachusetts Institute of Technology
    • MIT's intensive bootcamp on deep learning fundamentals, covering applications from computer vision to biology, with hands-on TensorFlow practice and a culminating project competition. Basic calculus and linear algebra knowledge required; Python experience beneficial.
  • CS25: Transformers United V2 - Stanford University
    • Explore the transformative power of transformers in deep learning across diverse domains, from NLP to biology, in a seminar featuring expert lectures, breakthrough discussions, and insights from leading researchers, aiming to foster understanding and cross-collaborative innovation.
  • Deep Learning Lecture Series 2020 - DeepMind x University College London
    • DeepMind presents a 12-lecture series on Deep Learning, diving from foundational topics to advanced techniques, encompassing areas from object recognition to responsible AI innovation, all delivered by leading research experts.
  • Reinforcement Learning Lecture Series 2021 - DeepMind x University College London
    • DeepMind and UCL present a comprehensive 13-lecture series on modern reinforcement learning, from foundational concepts to advanced deep RL techniques, led by expert researchers Hado van Hasselt, Diana Borsa, and Matteo Hessel.

πŸ’» AI System

πŸ—‚ Miscellaneous

✨ Star History

Star History Chart

🀝 Friendship Links

  • WayToAGI
    • WaytoAGI.com is the most comprehensive Chinese resource hub for AIGC, guiding users on an optimized learning journey to understand and harness the power of AI.
  • Codefuse-ChatBot
    • Codefuse-ChatBot is an open-source AI smart assistant designed to support the software development lifecycle with conversational access to tools, knowledge, and platform integration.
  • Codefuse DevOps Eval
    • DevOps-Eval is a GitHub repository offering a specialized suite for evaluating and improving foundation models in the DevOps sector, including a rich set of AIOps exercises.