* **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.

    *   Update sklearn imports for deprecated packages.
    *   Deprecate `Variable.count_up_to` and `tf.count_up_to` in favor of
        `Dataset.range`.
    *   Export `confusion_matrix` op as `tf.math.confusion_matrix` instead of
        `tf.train.confusion_matrix`.
    *   Add `tf.dtypes.` endpoint for every constant in dtypes.py. Moving
        endpoints in versions.py to corresponding endpoints in `tf.sysconfig.`

    assertEquals(int[].class, arrayOf(int.class).getRawType());
  }

  @SuppressWarnings("unused") // used by reflection
  private static class Holder<T> {
    List<T>[] matrix;

    void setList(List<T> list) {}
  }

  public void testWildcardCaptured_methodParameter_upperBound() throws Exception {
    TypeToken<Holder<?>> type = new TypeToken<Holder<?>>() {};

					<groupId>org.opensearch.plugin</groupId>
					<artifactId>mapper-extras-client</artifactId>
				</exclusion>
				<exclusion>
					<groupId>org.opensearch.plugin</groupId>
					<artifactId>aggs-matrix-stats-client</artifactId>
				</exclusion>
				<exclusion>
					<groupId>org.opensearch.plugin</groupId>
					<artifactId>rank-eval-client</artifactId>
				</exclusion>
				<exclusion>

jetzt

// jewelry : 2015-03-05 Binky Moon, LLC
jewelry

// jio : 2015-04-02 Reliance Industries Limited
jio

// jll : 2015-04-02 Jones Lang LaSalle Incorporated
jll

// jmp : 2015-03-26 Matrix IP LLC
jmp

// jnj : 2015-06-18 Johnson & Johnson Services, Inc.
jnj

// joburg : 2014-03-24 ZA Central Registry NPC trading as ZA Central Registry
joburg

* 🔧 Update MkDocs for other languages. PR [#9734](https://github.com/tiangolo/fastapi/pull/9734) by [@tiangolo](https://github.com/tiangolo).
* 👷 Refactor Docs CI, run in multiple workers with a dynamic matrix to optimize speed. PR [#9732](https://github.com/tiangolo/fastapi/pull/9732) by [@tiangolo](https://github.com/tiangolo).

* **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.

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