What is “pyenv”? pyenv is a popular tool used to manage multiple versions of Python on a single machine. It allows you to easily switch between different Python versions and manage project-specific Python environments. This can be particularly useful for developers who need to test their code across different Python versions or maintain projects that
本篇的內容來自 YC 今年夏季招募新創團隊的 RFS，我挑選了我很感興趣的領域，不感興趣或跟我專業差太遠的我就只把他們列出來而已，像是『新的防禦技術』、『將製造業帶回美國』、『新的太空公司』、『氣候技術』、『穩定幣金融』以及『終結癌症的方法』等等就跟我們現階段團隊技能組成幾乎完全沒相關，至於其他的幾項我都會沿著脈絡去探索我們公司的機會點在哪裡，我最後會就我目前最愛且最相關的那五個方向做深度的研究。
LLMOps（Large Language Model Operations）是一個涵蓋了大型語言模型（如 OpenAI 的 GPT 系列、Google 的 PaLM 以及眾多如 Meta 的 LLaMa 這樣的開源 LLMs）開發、部署、維護和優化的一整套實作的方法論和資料管理流程。LLMOps 的存在目標是確保讓開發者甚至一般使用者能夠高效能、高擴展性且安全地使用這些強大的 AI 模型來建構和營運能夠解決使用者問題的應用程式。整個架構牽涉到 #模型訓練、#微調、#RAG、部署、監控、版本控制、更新、安全性、可協作性、可轉移性以及合規性等各個面向。
Understanding ‘Encoders’ Visual Encoders Training Neural Networks Pre-trained Encoders Common Datasets for Pre-training Types of Pre-trained Visual Encoders Fine-tuning & Transfer Learning Applications Challenges & Considerations Conclusion Pre-trained visual encoders leverage prior knowledge from extensive datasets to offer a head start when tackling new visual tasks. They encapsulate a form of “transfer learning,” allowing models
Basic Understanding of Modality In the context of AI, a “modality” refers to a specific type of data or way of interacting. For example, text, images, audio, and videos are different modalities. Each modality offers a unique representation of information. What is Multi-Modality? Multi-modality involves combining information from multiple modalities to improve AI’s understanding, representation,
