English series
AI
English editions of Guozhen AI articles. The text is localized for global readers while the original diagrams, screenshots, and code examples remain aligned with the Chinese source.
Use this series as the technical reading layer, then continue into AI software buyer guides, tool comparisons, benchmarks, API platform decisions, coding agents, and LLM security research.
From Series Reading to Tool Decisions
Turn this AI series into practical software, model, API, and security choices.
English Series FAQ
Use this series as evidence before choosing AI tools.
How should I use the AI English series?
Use the series as the learning layer for concepts, screenshots, prompts, and implementation details, then continue into buyer guides, tool comparisons, benchmarks, API decisions, and security checks.
Is the AI series enough to choose an AI tool?
No. The series gives context and practical examples, but production choices still need pricing review, privacy checks, integration testing, benchmark evidence, and fallback planning.
What should I read after this 21-lesson series?
Open AI Software Buyer Guides, AI Tools Workbench, Best AI Coding Agents, AI Model Benchmarks, OpenAI vs Anthropic API, or LLM Security Tools depending on your next decision.
Why keep the original diagrams and screenshots?
The visuals preserve source evidence from the Chinese articles, so global readers can inspect interfaces, outputs, and workflows instead of relying only on a translated summary.
Simulate coin flips
Don’t stop learning probability theory at rote memorization of formulas. The most effective approach is to validate your intuition with small simulations—and then in...
Read lesson20. Probability for AI Beginners: Summarizing Model Evaluation & Selection + Further Learning Resources
Model selection shouldn’t simply chase the highest score. To identify a robust, production ready solution, you must compare baselines, validation strategies, error p...
Read lessonLoad dataset
Model evaluation is itself a probabilistic problem: prediction scores, decision thresholds, and misclassification costs jointly determine whether a model is fit for...
Read lessonDefine x-axis (conversion rate from 0 to 1)
When implementing Bayesian methods, the most critical step is to clearly separate prior assumptions from empirical evidence—and then assess whether the resulting pos...
Read lesson17. Bayesian Updating: Priors and Posteriors
Bayesian updating is not a one time formula—it is an ongoing process of assimilating evidence. Today’s posterior can serve as tomorrow’s prior.
Read lessonUnderstanding Bayes' Theorem
Bayes’ Theorem updates beliefs in light of new evidence. It combines three key elements: what we believed before seeing the evidence (prior), how strongly the eviden...
Read lesson15 Practical Applications of the Central Limit Theorem
The Central Limit Theorem (CLT) explains why sample means from many distributions tend to approximate a normal distribution. It forms the theoretical foundation for...
Read lessonSet random seed for reproducibility
The Law of Large Numbers tells us that, given a sufficiently large number of repetitions, the sample average will gradually converge toward the theoretical expectati...
Read lessonCompute variances
Covariance measures whether two variables change together ; correlation removes the influence of scale. A high correlation does not imply causation.
Read lessonDefine random variable X
Variance measures how much outcomes fluctuate around their expected value. Two models may share the same expectation, yet differ markedly in variance—leading to enti...
Read lessonDie face values
The expected value is the long run average weighted by probabilities—it need not be an outcome that actually occurs in any single trial. It serves well to measure ov...
Read lessonSet parameters
The geometric distribution models how long we must wait until the first success . It is appropriate for “waiting time” problems—not for counting how many successes o...
Read lessonCompute Poisson probability
The Poisson distribution is well suited for modeling the number of occurrences of an event within a fixed interval of time or space—such as arrivals, clicks, or fail...
Read lessonSet mean and standard deviation
The normal distribution is centered at its mean and characterized by its standard deviation, which quantifies dispersion. Many types of measurement errors and sample...
Read lessonVisualize the binomial PMF
The binomial distribution applies to a fixed number of repeated trials, where we care specifically about how many times success occurs . Its key requirements are:
Read lessonSet parameters
The PDF describes density ; the CDF describes cumulative probability . In continuous distributions, the density value at a single point does not equal the probabilit...
Read lessonSimulate 1000 die rolls
For discrete variables, we examine the probability at each point ; for continuous variables, we examine the area under the curve over an interval. The computational...
Read lessonDefine possible values of random variable X
A random variable transforms uncertain experimental outcomes into computable numbers. It serves as the bridge from events to distributions, expectations, and model e...
Read lessonSet seed for reproducibility
Conditional probability is not merely ordinary probability with an extra symbol—it redefines the scope of discussion given that a particular event has occurred.
Read lessonDefine the sample space
The sample space comprises all possible outcomes of an experiment; an event is a subset of that space. For many probability problems, the main difficulty lies not in...
Read lessonDefine counts
Probability first addresses a fundamental question: Among all possible outcomes, how likely is a particular event to occur? To make sense of probability formulas, we...
Read lesson