November 2017
Intermediate to advanced
328 pages
6h 51m
Japanese
Content preview from 入門 PySpark ―PythonとJupyterで活用するSpark 2エコシステム
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8.4
TensorFrames
のクイックスタート
169
TensorFlowを既存のデータに対して活用する
TensorFramesを使ってTensorFlowとApache Sparkを結合すれば、データサイエンティストは分
析、ストリーミング、グラフ処理、機械学習の処理に TensorFlowを使ってディープラーニングを取り
入れることができます。こうすれば、大規模な環境下でのモデルのトレーニングやデプロイが可能にな
ります。
最適なハイパーパラメータを決定するための並列トレーニング
ディープラーニングのモデルを構築する際には、モデルのトレーニングに影響するパラメータがいく
つかあります（ハイパーパラメータ）。ディープラーニングや人工ニューラルネットワークで一般的なの
は、学習レート（このレートが高ければ高速に学習を行うものの、変化の激しい入力を考慮に入れなく
なってしまうかもしれません。すなわち、データのレートと変動が高すぎると、学習がうまくいかなくな
るということです）と、ニューラルネットワーク中の各層のニューロン数（ニューロンが多すぎれば推
定にノイズが増え、少なすぎればネットワークがうまく学習しなくなります）です。
Deep Learning with Apache Spark and TensorFlow（https://databricks.com/blog/2016/01/25/deep-
learning-with-apache-spark-and-tensorflow.html
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