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

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

---

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

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

---

## **Typer**, FastAPI ti CLIs

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

## **Typer**, فریم‌ورکی معادل FastAPI برای کار با <abbr title="CLI (Command Line Interface)">واسط خط فرمان</abbr>

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

 *
 * <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

* **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.

* **Bilgisayar görüsü**: bir görüntü milyonlarca pikselden oluşur, her pikselin 3 değeri / rengi vardır, bu pikseller üzerinde aynı anda bir şeyler hesaplamayı gerektiren işleme.
* **Makine Öğrenimi**: Çok sayıda "matris" ve "vektör" çarpımı gerektirir. Sayıları olan ve hepsini aynı anda çarpan büyük bir elektronik tablo düşünün.

* **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.

* 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.

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