grouping_COSMO_Margules_all.ipynb 詳細分析報告
本 Jupyter Notebook 實作了一套物理約束神經網路 (PINN) 預測管線，用於預測氣液平衡 (VLE) 數據。方法結合了 COSMO 分子描述符與 Margules 活度係數模型。Notebook 載入預先訓練好的模型，針對二元混合物的熱力學性質（氣相組成 y 及系統壓力 P）進行預測，共約 17,600 筆數據。
阅读文章 →AI for Chemistry - 專案原理與運作說明
本專案是一個基於機器學習的化學性質預測系統，主要包含兩個子專案：: ChemProp_VP - 基於圖神經網絡（GNN）的分子性質預測模型，專注於蒸氣壓（Vapor Pressure）預測 2025_COSMO - 基於 COSMO（Conductor-like Screening Model）σ-profile 的汽液平衡（VLE）預測系統
阅读文章 →RAG + AI 完整教材
從零開始打造一套生產級 Retrieval-Augmented Generation（檢索增強生成）系統 > 主要技術棧：LangChain + OpenAI + Chroma；輔以 Ollama + FAISS 本地化方案 > 適合對象：Python 開發者、AI 應用工程師、資料工程師
阅读文章 →RAG AI 範例專案
依據 RAG_AI_完整教材.md 附錄 A 實作的生產級 PDF / Markdown 智能問答系統。
阅读文章 →范例科技 — 常見問題 FAQ
所有方案皆提供 14 天免費試用，試用期間不需信用卡資訊。
阅读文章 →范例科技股份有限公司員工手冊
版本 2024.10 — 本手冊為虛構範例資料，供 RAG 系統測試用。
阅读文章 →SSD — Sitio Web Portfolio
网站名称： 个人作品集网站（Portfolio Personal）
阅读文章 →Chijia · Portfolio personal
Diseño donde los humanos aterrizan suavemente. > 设计，让人轻轻落脚的地方。
阅读文章 →Detailed Analysis of grouping_COSMO_Margules_all.ipynb
This Jupyter notebook implements a Physics-Informed Neural Network (PINN) prediction pipeline for Vapor-Liquid Equilibrium (VLE) data using COSMO-based molecular descriptors combined with the Margules activity coefficien
阅读文章 →chemprop_VP
Note: These environments include a CPU-only version of PyTorch. If GPU acceleration is desired, please refer to the PyTorch homepage for the correct version and installation command. The recommended PyTorch version is 1.
阅读文章 →AI for Chemistry - 問題分析與改良方案
問題描述： 多處使用硬編碼的絕對路徑，嚴重影響可移植性。
阅读文章 →Software Requirements Specification (SRS)
Project: Pediatric Q&A via LINE (Python Flask + LINE + RAG with OpenAI)
阅读文章 →Comprehensive Machine Learning & Deep Learning Curriculum
A complete set of professional, textbook-quality teaching PDFs covering a depth-first path from mathematical foundations to production deep learning — written for someone who already codes well but wants real understandi
阅读文章 →How to use this curriculum
This is a depth-first path written for someone who already programs well. The assumption is Python comfort and solid software-engineering instincts; the gap we are closing is the conceptual and mathematical one. Every ph
阅读文章 →Bayesian Inference — Preface: how to use this book
There is one equation behind this entire book, and it fits on a single line
阅读文章 →Distributions, Conjugate Pairs, and Formulas
A compact reference to the book's key results. @p4.8cmp9.2cm@ Quantity & Formula Bayes' theorem & P( D)=P(D ) P( )P(D) Working form & p( D) p(D ) p( ) Evidence (marginal likelihood) & p(D)= p(D ) p( ) d Posterior predict
阅读文章 →What Is Bayesian Thinking?
Before any formula, Bayesian inference is a way of reasoning: hold beliefs as probabilities, and revise them in proportion to the evidence. This chapter sets up that worldview — probability as degree of belief, the contr
阅读文章 →Bayes' Theorem
Now we make the update rule exact. Bayes' theorem is a short consequence of the definition of conditional probability, but its implications are deep enough to have reshaped statistics, science, and AI. This chapter deriv
阅读文章 →The Prior, the Likelihood, and the Posterior
In Chapters – the hypotheses were discrete (sick or healthy). The real power of Bayesian inference appears when the unknown is a continuous parameter — a probability, a rate, a mean — so the prior and posterior become di
阅读文章 →Conjugate Priors and Updating
Computing the posterior means doing the integral p(D)= p(D )p( ) d — usually hard. Conjugate priors are a beautiful special case where the integral is free: prior and posterior belong to the same family, so updating is j
阅读文章 →Choosing Priors
The prior is what makes Bayesian inference both powerful and controversial: it lets you inject knowledge, but it also forces you to be explicit about your assumptions. This chapter is a practical guide to choosing priors
阅读文章 →Summarizing the Posterior and Bayesian Decisions
The posterior is a full distribution, but people need answers: a single estimate, an interval, a yes/no decision. This chapter shows how to summarize a posterior responsibly (point estimates and credible intervals) and h
阅读文章 →Naive Bayes Classifiers
Naive Bayes is Bayes' theorem turned into a fast, surprisingly effective classifier. Despite an assumption that is almost always false — that features are independent given the class — it powers spam filters, document cl
阅读文章 →Bayesian versus Frequentist Inference
The two great schools of statistics answer the same questions in fundamentally different ways. Understanding the contrast sharpens your grasp of both — and clears up the most persistent confusions in applied statistics,
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