*
 * <p>This class should not be assumed to be universally superior to {@code java.util.HashSet}.
 * Generally speaking, this class reduces object allocation and memory consumption at the price of
 * moderately increased constant factors of CPU. Only use this class when there is a specific reason
 * to prioritize memory over CPU.
 *
 * @author Dimitris Andreou
 * @author Jon Noack
 */
@GwtIncompatible // not worth using in GWT for now

* **Aprendizado de Máquina**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande planilha com números e em multiplicar todos eles juntos e ao mesmo tempo.

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

## **Typer**, CLI를 위한 FastAPI { #typer-the-fastapi-of-clis }

<a href="https://typer.tiangolo.com"><img src="https://typer.tiangolo.com/img/logo-margin/logo-margin-vector.svg" style="width: 20%;"></a>

웹 API 대신 터미널에서 사용할 <abbr title="Command Line Interface - 커맨드 라인 인터페이스">CLI</abbr> 앱을 만들고 있다면, [**Typer**](https://typer.tiangolo.com/)를 확인해 보십시오.

 *
 * <p>This class should not be assumed to be universally superior to {@code java.util.HashMap}.
 * Generally speaking, this class reduces object allocation and memory consumption at the price of
 * moderately increased constant factors of CPU. Only use this class when there is a specific reason
 * to prioritize memory over CPU.
 *
 * @author Louis Wasserman
 * @author Jon Noack
 */
@GwtIncompatible // not worth using in GWT for now

                            plan.step(p, AFTER + n2).ifPresent(after::executeAfter);
                        });
                    }
                });
            });

            // Keep projects in reactors by GAV
            Map<String, MavenProject> reactorGavs =
                    projects.keySet().stream().collect(Collectors.toMap(BuildPlanExecutor::gav, p -> p));

            // Go through all plugins

* **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Piensa en una enorme hoja de cálculo con números y multiplicando todos juntos al mismo tiempo.

		for {
			tok = p.nextToken()
			if len(operands) == 0 && len(items) == 0 {
				if p.arch.InFamily(sys.ARM, sys.ARM64, sys.AMD64, sys.I386, sys.Loong64, sys.RISCV64) && tok == '.' {
					// Suffixes: ARM conditionals, Loong64 vector instructions, RISCV rounding mode or x86 modifiers.
					tok = p.nextToken()
					str := p.lex.Text()
					if tok != scanner.Ident {
						p.errorf("instruction suffix expected identifier, found %s", str)
					}

* L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.

* **Makine Öğrenimi**: genellikle çok sayıda "matris" ve "vektör" çarpımı gerekir. Sayılar içeren devasa bir elektronik tabloyu ve hepsini aynı anda çarpmayı düşünün.