July 2020
Intermediate to advanced
852 pages
25h 44m
German
book
Praxiseinstieg Machine Learning mit Scikit-Learn, Keras und TensorFlow, 2nd Edition
by Aurélien Géron
Content preview from Praxiseinstieg Machine Learning mit Scikit-Learn, Keras und TensorFlow, 2nd Edition
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ANHANG F
Spezielle Datenstrukturen
In diesem Anhang wollen wir uns kurz die über normale Float- oder Integer-Tensoren hinausgehenden Datenstrukturen anschauen, die von TensorFlow unterstützt werden. Dazu gehören Strings, Ragged-Tensoren, Sparse-Tensoren, Tensor-Arrays, Sets und Queues.
Strings
Tensoren können Bytestrings enthalten, was besonders für die Verarbeitung natürlicher Sprache nützlich ist (siehe Kapitel 16):
>>> tf.constant(b"hello world")
<tf.Tensor: id=149, shape=(), dtype=string, numpy=b'Hallo Welt'>
Versuchen Sie, einen Tensor mit einem Unicodestring zu erstellen, konvertiert TensorFlow ihn automatisch nach UTF-8:
>>> tf.constant("café")
<tf.Tensor: id=138, shape=(), dtype=string, numpy=b'caf\xc3\xa9'>
Es ist auch möglich, Tensoren ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.