IA e Machine Learning
Machine learning, geração aumentada por recuperação (RAG), aplicações de IA e web crawling.
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
Ler artigo →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.
Ler artigo →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。
Ler artigo →Software Requirements Specification (SRS)
Project: Pediatric Q&A via LINE (Python Flask + LINE + RAG with OpenAI)
Ler artigo →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
Ler artigo →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
Ler artigo →Bayesian Inference — Preface: how to use this book
There is one equation behind this entire book, and it fits on a single line
Ler artigo →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
Ler artigo →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
Ler artigo →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
Ler artigo →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
Ler artigo →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
Ler artigo →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
Ler artigo →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
Ler artigo →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
Ler artigo →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,
Ler artigo →Bayesian Networks and Graphical Models
Real problems have many interacting variables. Bayesian networks (directed graphical models) make such joint distributions tractable by drawing the dependencies as a graph: an arrow means “directly influences,” a missing
Ler artigo →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: MCMC draws samples from the posterior wit
Ler artigo →Bayesian Regression and Hierarchical Models
Now we put the machinery to work on the models practitioners use every day. Bayesian regression replaces point-estimate coefficients with full posteriors, delivering principled uncertainty on every prediction. Hierarchic
Ler artigo →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 evidence p(D) — which turns out to automa
Ler artigo →Bayes in Machine Learning, Deep Learning, and AI
This capstone gathers the threads into one claim: Bayesian reasoning is the theory of learning under uncertainty, and it quietly underwrites much of machine learning. The loss is a likelihood. Regularization is a prior.
Ler artigo →Notation and Formula Reference
A compact reference for the symbols, rules, and formulas used throughout the book.
Ler artigo →Functions and the Idea of Calculus
Calculus studies how things change and how things accumulate. Before we can speak of change, we need the object that changes: the function. This chapter sets up functions and the handful of “essential” ones that appear e
Ler artigo →Limits and Continuity
The limit is the idea that makes calculus rigorous. “Instantaneous” rate of change and “infinitely thin” rectangles both hide a limit: a quantity we approach but may never reach. This chapter builds limits intuitively an
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