labels.purgeSuggestSearchLogDay=Eski öneri bilgilerini sil
labels.q=Sorgu
labels.roles=Roller
labels.suggestSearchLog=Öneri Arama Günlüğü
labels.suggestWord=Öneri Kelime
labels.targetLabel=Etiket
labels.term=Terim
labels.fields=Alanlar
labels.ex_q=Genişletilmiş Sorgu
labels.oldPassword=Mevcut Şifre
labels.newPassword=Yeni Şifre
labels.confirmNewPassword=Yeni Şifre (Onayla)

labels.menu_system=Sistem
labels.menu_wizard=Sihirbaz

* **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.
* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

func getQuorumDisks(disks []StorageAPI, infos []DiskInfo, readQuorum int) (newDisks []StorageAPI) {
	newDisks, _ = getQuorumDiskInfos(disks, infos, readQuorum)
	return newDisks
}

// Will return io.EOF if continuing would not yield more results.
func (er *erasureObjects) listPath(ctx context.Context, o listPathOptions, results chan<- metaCacheEntry) (err error) {
	defer xioutil.SafeClose(results)
	o.debugf(color.Green("listPath:")+" with options: %#v", o)

		}
	}
	if v == "" {
		if !ctx.Args().Present() || ctx.Args().First() == "help" {
			cli.ShowCommandHelpAndExit(ctx, ctx.Command.Name, 1)
		}
		return ctx.Args()
	}
	return strings.Fields(v)
}

func configCommonToSrvCtx(cf config.ServerConfigCommon, ctxt *serverCtxt) {
	ctxt.RootUser = cf.RootUser
	ctxt.RootPwd = cf.RootPwd

	if cf.Addr != "" {
		ctxt.Addr = cf.Addr
	}

labels.q=Запрос
labels.roles=Роли
labels.suggestSearchLog=Журнал поиска предложений
labels.suggestWord=Предложить слово
labels.targetLabel=Метка
labels.term=Термин
labels.fields=Поля
labels.ex_q=Расширенный запрос
labels.oldPassword=Текущий пароль
labels.newPassword=Новый пароль
labels.confirmNewPassword=Новый пароль (подтверждение)

labels.menu_system=Система
labels.menu_wizard=Мастер

  // the "short hash".
  //
  // The `keys`, `values`, and `entries` arrays always have the same size as each other. They can be
  // seen as fields of an imaginary `Entry` object like this:
  //
  // class Entry {
  //    int hash;
  //    Entry next;
  //    K key;
  //    V value;
  // }
  //

        "protocols doesn't contain http/1.1: $protocolList"
      }
      require(null !in protocolList as List<Protocol?>) { "protocols must not contain null" }
      field = protocolList
    }

  public val started: Boolean
    get() = socketAddress_ != null

  /**
   * Returns a URL for connecting to this server.
   *
   * @param path the request path, such as "/".

    checkEntry(entry1, "custom-bar", "custom-header", 55)
    val entry2 = hpackReader!!.dynamicTable[readerHeaderTableLength() - 2]!!
    checkEntry(entry2, "custom-baz", "custom-header", 55)

    // Once a header field is decoded and added to the reconstructed header
    // list, it cannot be removed from it. Hence, foo is here.
    assertThat(hpackReader!!.getAndResetHeaderList()).isEqualTo(headerBlock)

			accessKey:          credentials.AccessKey,
			secretKey:          credentials.SecretKey,
			expectedRespStatus: http.StatusBadRequest,
		},
		// Test case - 10.
		// Existent bucket with policy with Version field empty.
		// writing BucketPolicy should fail.
		// should result in 400 StatusBadRequest.
		{
			bucketName:         bucketName,

labels.q=Consulta
labels.roles=Funções
labels.suggestSearchLog=Log de pesquisa para sugestões
labels.suggestWord=Palavra sugerida
labels.targetLabel=Rótulo
labels.term=Termo de pesquisa
labels.fields=Campos
labels.ex_q=Consulta estendida
labels.oldPassword=Senha atual
labels.newPassword=Nova senha
labels.confirmNewPassword=Confirmar nova senha
labels.menu_system=Sistema
labels.menu_wizard=Assistente