Python Pandasis an open-source toolkit which provides data scientists and analysts with data manipulation and analysis capabilities using the Python programming language. The Pandas library is very popular in the preprocessing phase of machine learning and deep learning. But now you can do more with ...
Diffusers is a Python library for diffusion models, which are a type of generative model that can be used to generate images, audio, and other types of data. Developed by Hugging Face, this library provides a seamless way to create stunning visuals using generative AI. Diffusers provides a va...
FAIR AI Insights Explore our insightful AI/ML resources for Foundry for AI by Rackspace (FAIR) to unlock the path of creativity and discovery of AI. Resources Explore our vast content library and find deep dive assets like whitepapers, infographics, e-books, videos, data sheets and more. ...
Generative AI 新世界 | 文生图领域动手实践:预训练模型的部署和推理 在上期文章,我们探讨了文生图(Text-to-Image)方向的主要论文解读,包括:VAE、DDPM、DDIM、GLIDE、Imagen、UnCLIP、CDM、LDM 等主要扩散模型领域的发展状况。 本期我们将进入动手实践环节,我会带领大家使用 Amazon SageMaker Studio、Amazon SageMaker ...
我们将使用 Amazon SageMaker python SDK 将 Falcon 40B 部署到终端节点用于模型推理。我们首先需要确保正确安装了 Amazon SageMaker python SDK。如下代码所示: # install supported sagemaker SDK !pip install "sagemaker>=2.175.0" --upgrade –quiet import sagemaker ...
Updated May 14, 2025 Python genieincodebottle / generative-ai Star 975 Code Issues Pull requests Comprehensive resources on Generative AI, including a detailed roadmap, projects, use cases, interview preparation, and coding preparation. ai openai llama gemini-api transformer-architecture gpt-4 ...
Pandas AI is a Python library that uses generative AI models to supercharge pandas capabilities. It was created to complement the pandas library, a widely-used tool for data analysis and manipulation. Users can summarize pandas data frames data by using natural language. Moreover, you can use ...
The official Python library for the Google Gemini API - Issues · google-gemini/generative-ai-python
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生成式AI项目生命周期备忘单(Generative AI Project Lifecycle Cheat Sheet) 1. 选择模型、微调模型以及使模型与人类偏好相一致: 2. 大型语言模型(Large Language Model, LLM)的预训练阶段: 3. 评估模型性能: 4. 提示调优和微调(Prompt Tuning and Fine Tuning): 5. 使用人类反馈的强化学习来调整模型(Aligning ...