August 2018
Intermediate to advanced
336 pages
5h 9m
Japanese
Content preview from 直感 Deep Learning ―Python×Kerasでアイデアを形にするレシピBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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1章ニューラルネットワークの基礎
人工ニューラルネットワーク（略してニューラルネットワーク）は機械学習モデルの一種であり、哺乳類の中枢神経の研究から影響を受けました。各ネットワークは相互接続されたニューロンで構成されており、特定の条件を満たしたときにメッセージを交換します。これは、専門用語で発火と呼ばれています。
1950年代後半に初期の研究が始まり、パーセプトロンが生まれました。パーセプトロンは2層のネットワークであり、単純な演算に用いることを目的としていました（詳細については、F. Rosenblattの“The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain”，Psychological Review, vol.65, pp.386-408, 1958を参照してください）。
1960年代後半には、多層のネットワークを効率的に学習するために用いられる誤差逆伝播法（backpropagation）というアルゴリズムが導入されました（詳細については、P.J. Werbosの“Backpropagation through Time: What It Does and How to Do It”，Proceedings of the IEEE, vol.78, pp.1550-1560, 1990やG.E. Hinton、S. Osindero、Y.W. Tehの“A Fast Learning Algorithm for Deep Belief Nets”，Neural Computing, vol.18, pp.1527-1554, 2006を参照してください）。 ...
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