Awesome AIGC Tutorials
Section: Prompt Engineering · This guide introduces Prompt Engineering, a discipline that optimizes interactions with Large Language Models, offering extensive resources, research, and tools.
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This guide introduces Prompt Engineering, a discipline that optimizes interactions with Large Language Models, offering extensive resources, research, and tools.
Section: Prompt Engineering · This guide introduces Prompt Engineering, a discipline that optimizes interactions with Large Language Models, offering extensive resources, research, and tools.
Section: Guides · A comprehensive guide that contains all the latest papers, learning resources, and developments in the field of prompt engineering.
Section: AI
Section: 教程 Tutorial
Section: Tools & Libraries · Comprehensive prompt engineering reference (DAIR-AI)
robot: The free, Open Source alternative to OpenAI, Claude and others. Self-hosted and local-first. Drop-in replacement for OpenAI, running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. Features: Generate Text, Audio, Video,…
Test your prompts, models, RAGs. Evaluate and compare LLM outputs, catch regressions, and improve prompt quality. LLM evals for OpenAI/Azure GPT, Anthropic Claude, VertexAI Gemini, Ollama, Local & private models like Mistral/Mixtral/Llama with CI/CD
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
OpenAI's cookbook includes examples of prompt engineering.
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.
New series of 5 Deep Learning courses by Andrew Ng, now with Python rather than Matlab/Octave, and which leads to a specialization certificate.
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.
10-week, 20-lesson curriculum on data science fundamentals. Covers data preparation, visualization, modeling, and deployment with practical projects.