Detailed Analysis of groupingCOSMOMargulesall.ipynb
This Jupyter notebook implements a PhysicsInformed Neural Network PINN prediction pipeline for VaporLiquid Equilibrium VLE data using COSMObased molecular descriptors combined with the Margules activity coefficient model
記事を読む →groupingCOSMOMargulesall.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。
記事を読む →chempropVP
Note: These environments include a CPUonly version of PyTorch. If GPU acceleration is desired, please refer to the PyTorch homepagehttps://pytorch.org for the correct version and installation command. The recommended PyT
記事を読む →AI for Chemistry 問題分析與改良方案
1. 程式碼品質問題程式碼品質問題 2. 架構設計問題架構設計問題 3. 資料處理問題資料處理問題 4. 模型設計問題模型設計問題 5. 訓練流程問題訓練流程問題 6. 可擴展性問題可擴展性問題 7. 綜合改良建議綜合改良建議
記事を読む →AI for Chemistry 專案原理與運作說明
1. 專案概述專案概述 2. 基礎理論基礎理論 3. 系統架構系統架構 4. 資料處理流程資料處理流程 5. 模型架構詳解模型架構詳解 6. 訓練與預測流程訓練與預測流程 7. 技術細節技術細節
記事を読む →Software Requirements Specification SRS
Project: Pediatric Q&A via LINE Python Flask + LINE + RAG with OpenAI
記事を読む →Comprehensive Machine Learning & Deep Learning Curriculum
A complete set of professional, textbookquality teaching PDFs covering a depthfirst path from mathematical foundations to production deep learning — written for someone who already codes well but wants real understanding
記事を読む →How to use this curriculum
Machine Learning Deep LearningA depth-first curriculum, from math foundations to production9--14 months at 8--12 hrs/weekTurn ``I can call '' into ``I understand, can implement, and can ship.'' This guide is the map for
記事を読む →Preface: how to use this book
There is one equation behind this entire book, and it fits on a single line: [ P( D)=P(D),. ] is a recipe for learning from evidence: start with what you believe (the ), see how well each possibility explains the data (t
記事を読む →Distributions, Conjugate Pairs, and Formulas
1.5 @p4.8cmp9.2cm@ Bayes' theorem P( D)=P(D) Working form p( D) p(D),p() Evidence (marginal likelihood) p(D)= p(D),p(),d Posterior predictive p( x D)= p( x),p( D),d MAP estimate =_ Bayes action a=_a _ D[L(,a)] Bayes fact
記事を読む →What Is Bayesian Thinking?
Before any formula, Bayesian inference is a : hold beliefs as probabilities, and revise them in proportion to the evidence. This chapter sets up that worldview --- probability as degree of belief, the contrast with the f
記事を読む →Bayes' Theorem
Now we make the update rule exact. 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 derives it, names it
記事を読む →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 --- a probability, a rate, a mean --- so the prior and posterior become . This chapter is t
記事を読む →Conjugate Priors and Updating
Computing the posterior means doing the integral p(D)= p(D)p(),d --- usually hard. are a beautiful special case where the integral is free: prior and posterior belong to the same family, so updating is just . The three c
記事を読む →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
記事を読む →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 a posterior responsibly (point estimates and credible intervals) and how Bayesia
記事を読む →Naive Bayes Classifiers
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 classifier
記事を読む →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 --- and clears up the most persistent confusions in applied statistics, lik
記事を読む →Bayesian Networks and Graphical Models
Real problems have many interacting variables. (directed graphical models) make such joint distributions tractable by drawing the dependencies as a graph: an arrow means ``directly influences,'' a missing arrow means ``c
記事を読む →Computational Bayes: MCMC and Variational Inference
Conjugacy is a luxury; most real posteriors have no closed form because the evidence integral p(D)= p(D)p(),d is intractable. The modern Bayesian toolkit sidesteps it two ways: draws samples from the posterior without ev
記事を読む →Bayesian Regression and Hierarchical Models
Now we put the machinery to work on the models practitioners use every day. replaces point-estimate coefficients with full posteriors, delivering principled uncertainty on every prediction. go further, sharing informatio
記事を読む →Model Comparison, Evidence, and Occam's Razor
Inference within a model assumes the model is right. But which model should we use? Bayesian model comparison answers this with the very quantity we worked so hard to avoid --- the p(D) --- which turns out to automatical
記事を読む →Bayes in Machine Learning, Deep Learning, and AI
This capstone gathers the threads into one claim: . The loss is a likelihood. Regularization is a prior. Training finds a posterior mode. Ensembles approximate posterior averaging. Uncertainty estimates are posteriors. F
記事を読む →Notation and Formula Reference
A compact reference for the symbols, rules, and formulas used throughout the book.
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