* **Maschinelles Lernen**: Normalerweise sind viele „Matrix“- und „Vektor“-Multiplikationen erforderlich. Stellen Sie sich eine riesige Tabelle mit Zahlen vor, in der Sie alle Zahlen gleichzeitig multiplizieren.

			Prefixes:    []string{"dir/day_id=2017-10-10/"},
		},
		// ListObjectsResult-37 list with prefix match 2 levels deep
		37: {
			IsTruncated: false,
			Objects: []ObjectInfo{
				{Name: "foo/201910/1112"},
				{Name: "foo/201910/1122"},
			},
		},
		// ListObjectsResult-38 list with prefix match 1 level deep
		38: {
			IsTruncated: false,
			Objects: []ObjectInfo{
				{Name: "foo/201910/1112"},

  The rabbit-hole went straight on like a tunnel for some way,
and then dipped suddenly down, so suddenly that Alice had not a
moment to think about stopping herself before she found herself
falling down a very deep well.

  Either the well was very deep, or she fell very slowly, for she
had plenty of time as she went down to look about her and to
wonder what was going to happen next.  First, she tried to look

i10/fastapi_cheatsheet title: A FastAPI and Swagger UI visual cheatsheet - author: Uber Engineering author_link: https://eng.uber.com link: https://eng.uber.com/ludwig-v0-2/ title: 'Uber: Ludwig v0.2 Adds New Features and Other Improvements to its Deep Learning Toolbox [including a FastAPI server]' - author: Maarten Grootendorst author_link: https://www.linkedin.com/in/mgrootendorst/ link: https://towardsdatascience.com/how-to-deploy-a-machine-learning-model-dc51200fe8cf title: How to Deploy a Machine...

         .add("publicobject.com",   "sha1/T5x9IXmcrQ7YuQxXnxoCmeeQ84c=")
         .add("*.publicobject.com", "sha1/T5x9IXmcrQ7YuQxXnxoCmeeQ84c=")
         .build());
    ```

 *  **Interceptors lists are now deep-copied by `OkHttpClient.clone()`.**
    Previously clones shared interceptors, which made it difficult to customize
    the interceptors on a request-by-request basis.

 *  New: `Headers.toMultimap()`.

 * href="https://www.javadoc.io/doc/com.github.ben-manes.caffeine/caffeine/latest/com.github.benmanes.caffeine/com/github/benmanes/caffeine/cache/AsyncLoadingCache.html">their
 * deep support for asynchronous operations</a>. But if you want to migrate to Caffeine with minimal
 * code changes, you can use <a

     * is the only one in use, but if we encounter nested concatenations, we start a deque of
     * meta-iterators rather than letting the nesting get arbitrarily deep.  This keeps each
     * operation O(1).
     */

    @CheckForNull private Iterator<? extends Iterator<? extends T>> topMetaIterator;

    // Only becomes nonnull if we encounter nested concatenations.

 * href="https://www.javadoc.io/doc/com.github.ben-manes.caffeine/caffeine/latest/com.github.benmanes.caffeine/com/github/benmanes/caffeine/cache/AsyncLoadingCache.html">their
 * deep support for asynchronous operations</a>. But if you want to migrate to Caffeine with minimal
 * code changes, you can use <a

            and newer CPUs.)
            *   [Example performance speedups.](https://medium.com/intel-analytics-software/leverage-intel-deep-learning-optimizations-in-tensorflow-129faa80ee07)
        *   For older CPUs, oneDNN optimizations are disabled by default.
    *   **Windows x86 package:** oneDNN optimizations are disabled by default.

Shinsuke Sugaya <******@****.***> 1523506789 +0900