IA e Machine Learning
Machine learning, geração aumentada por recuperação (RAG), aplicações de IA e web crawling.
Tensors and Matrix Calculus
Two ideas carry linear algebra the last mile into deep learning. First, tensors generalize vectors and matrices to higher-order arrays — the natural container for images, sequences, and batches. Second, matrix calculus e
Ler artigo →Linear Algebra in Machine Learning, Deep Learning, and AI
This final chapter is the payoff. Every concept in the book reappears here as the working machinery of modern AI. The thesis is simple and, by now, earned: linear algebra is the language in which machine learning is writ
Ler artigo →Preface: how to use this book
Linear algebra is the mathematics of many numbers at once. The moment you stop thinking about a single quantity and start thinking about lists of quantities — the pixels of an image, the features of a customer, the weigh
Ler artigo →Why this phase matters
Almost everyone who “bounces off” machine learning does so for the same reason: they tried to learn the algorithms before they had the language the algorithms are written in. That language is three branches of mathematic
Ler artigo →Orientation
Classical (“tabular”) machine learning is still where most production value is created outside of the LLM frontier: churn prediction, fraud, pricing, demand forecasting, ranking. It is also the best place to build the di
Ler artigo →From linear models to neural networks
A neural network is a stack of learned feature transformations followed by a linear model. Each layer applies an affine map then a nonlinearity; composing many of them lets the network represent functions that no single
Ler artigo →Why images need special architectures
An image is a grid of pixels with strong spatial structure: nearby pixels are correlated, and patterns (edges, textures, objects) can appear anywhere. A plain MLP ignores this — it would need a separate weight for every
Ler artigo →Why this is the highest-leverage phase
The Transformer is the single most important architecture in modern machine learning. It powers large language models, underpins modern vision (ViT, Phase 3), audio, code, and multimodal systems, and is where the overwhe
Ler artigo →Discriminative vs. generative
Everything so far has been mostly discriminative: learn p(y ) to predict a label. Generative models learn the data distribution p( ) itself, so you can sample new examples that look like the training data — images, audio
Ler artigo →A different learning paradigm
Supervised learning has labeled answers; reinforcement learning (RL) has only reward. An agent takes actions in an environment, which returns a new state and a scalar reward; the agent must learn a behavior (policy) that
Ler artigo →Why MLOps is your edge
A model in a notebook creates zero value. Value comes from a model that serves predictions reliably, cheaply, and correctly — and keeps doing so as data shifts. This is software and systems engineering with a few ML-spec
Ler artigo →The purpose of capstones
Everything before this phase built capability; capstones prove and compound it. A finished, deployed project that solves a real problem teaches more — and signals more — than another course completed. The strategic move
Ler artigo →Probability & Statistics — Preface: how to use this book
If linear algebra is the language of machine learning and calculus is its engine, then probability and statistics are its worldview. Machine learning is, at its core, the discipline of drawing reliable conclusions from n
Ler artigo →Distributions and Identities Reference
A compact reference for the notation, distributions, and identities used throughout the book.
Ler artigo →What Is Probability?
Probability is the mathematics of uncertainty — a precise language for reasoning about things we cannot predict with certainty. A coin flip, tomorrow's weather, whether an email is spam, what word comes next in a sentenc
Ler artigo →Conditional Probability and Bayes' Theorem
Almost all useful probability is conditional: we want to know the chance of something given what we already know. How likely is disease given a positive test? Spam given the word “free”? The next word given the sentence
Ler artigo →Random Variables and Expectation
So far events have been qualitative (“it rained”). A random variable attaches numbers to outcomes, letting us do arithmetic with uncertainty: average it, measure its spread, add independent copies. Random variables and t
Ler artigo →Common Probability Distributions
A handful of distributions describe most of the randomness we ever model. Each arises from a simple, recurring story — a coin flip, a count of rare events, a sum of many small effects — and learning those stories lets yo
Ler artigo →Joint Distributions, Covariance, and the Multivariate Gaussian
Real data is rarely one number; it is vectors of many interrelated quantities — pixels, features, tokens. To model them we need joint distributions that capture how variables vary together, the covariance that quantifies
Ler artigo →Inequalities and Limit Theorems
Why does averaging more data give a better answer? Why are so many things bell-shaped? Why can we estimate an expectation by a sample mean — the move that justifies mini-batch training? The limit theorems answer these qu
Ler artigo →Estimation and Maximum Likelihood
We now cross from probability into statistics — from “given the model, predict the data” to “given the data, infer the model.” The central task is estimation: using a sample to guess an unknown parameter. The dominant me
Ler artigo →Confidence Intervals and Hypothesis Testing
A point estimate alone is dangerous: it hides its own uncertainty. This chapter adds the error bars. Confidence intervals express how precise an estimate is; hypothesis tests decide whether an observed effect is real or
Ler artigo →Bayesian Inference
Frequentist statistics treats parameters as fixed unknowns and data as random. Bayesian inference flips the emphasis: it treats parameters as random variables with probability distributions that encode our beliefs, and u
Ler artigo →Statistical Models and Regression
Regression is where probability and statistics become predictive machine learning. A statistical model describes how outputs depend on inputs plus randomness; regression fits that dependence to data. Linear and logistic
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