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    5. 深度學習|使用Keras

    深度學習|使用Keras

    Author Rowel Atienza
    Publisher 碁峰資訊 /碁峰
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    Released
    2020/04/30
    Language
    Traditional Chinese
    File format
    PDF (28MB), fit in large screen and Pubook
    Pages
    369
    ID
    204460
    ISBN
    9789865025076
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    • Intro

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    本書將帶領您認識各種進階的深度學習技術,以及如何建立您專屬的劃時代AI。透過Keras完成各種實做專題,您會知道如何運用最新技術來建立高效率AI服務。

      本書將會介紹MLP、CNN與RNN等神經網路,這些是諸多進階技術的基石。藉由本書,您可以了解如何運用Keras與Tensorflow來實作深度學習。本書也會帶領您深入探討深度神經網路架構,包括ResNet、DenseNet以及自動編碼器。

      本書後半著眼於各種對抗生成網路(GAN),以及為什麼它們可以讓AI效能更上一層樓。實作變分編碼器(VAE)之後,您就能理解如何運用GAN與VAE強大的生成能力,並合成出讓人類信以為真的合成資料。最後介紹的是深度強化學習(DRL),例如深度Q學習與策略梯度方法等等,這些對於近年AI的發展上至關重要。

    本書特色

      .讓AI效能足以比美人類的各種尖端技術
      .使用Keras實作各種進階深度學習模型
      .各種進階技術的基石 - MLP、CNN與RNN
      .深度神經網路 – ResNet與DenseNet
      .自動編碼器與變分編碼器(VAE)
      .生成對抗網路(GAN)與各種嶄新的AI技術
      .抽離語義特徵GAN與跨域GAN
      .深度強化學習(DRL)的理論與實作
      .使用 OpenAI gym 建立符合業界標準的應用
      .深度Q學習與策略梯度方法
    More
    More
    第1章|認識進階深度學習與Keras
    介紹了深度學習領域的重要觀念,例如最佳化、正規化、損失函數、常用的網路與層以及如何用Keras來實作。本章也使用了Sequential API複習了深度學習與Keras。

    第2章|深度神經網路
    介紹Keras的Functional API,並使用這個API來在Keras中驗證並實作兩款常用的深度網路架構:ResNet與 DenseNet。

    第3章|自動編碼器
    介紹自動編碼器這個常見的網路架構,可用來找出輸入資料中潛在表示。本章使用Keras來討論並實作了自動編碼器的兩種應用:降噪與上色。

    第4章|生成對抗網路GAN
    介紹了當前深度學習領域最重要的進展。GAN可用於生成全新的合成資料,看起來和真的一樣。本章介紹了GAN的基本原理,並使用Keras來實作了兩種GAN:DCGAN與CGAN。

    第5章|各種改良版GAN
    介紹用於改良基礎GAN的各種演算法。這些演算法解決了訓練GAN時的難點,並提升了合成資料的品質。本章介紹了WGAN、LSGAN與ACGAN,並用Keras來實作。

    第6章|抽離語義特徵GAN
    討論了如何控制GAN所產生之合成資料的各種屬性。在抽離了潛在特徵之後,就可以控制所要的屬性了。本章介紹了兩種抽離語義特徵技術:InfoGAN與StackedGAN,並用Keras來實作。

    第7章|跨域GAN
    介紹GAN的一項實務應用:將某個領域的影像轉譯到另一個領域,也就是俗稱的跨域轉換。本章一樣使用Keras來討論並實作了CycleGAN這款廣泛運用的跨域GAN,另外也示範了如何使用CycleGAN來進行上色與風格轉換。

    第8章|變分自動編碼器
    類似於GAN,VAE也是一款能夠產生合成資料的生成模型。但又有點不一樣,VAE專攻可解碼的連續型潛在空間,適合用於進行變分推論。本章也介紹並用Keras實作了VAE與其變形款:CVAE and β -VAE。

    第9章|深度強化學習
    介紹強化學習與Q學習的運作原理,說明了兩種在離散型動作空間中實作Q-學習的技術:Q表更新與深度Q網路(DQN)。接著,使用Python來實作Q-學習以及用Keras來實作DQN,兩者都是在OpenAI gym環境中來完成。

    第10章|策略梯度方法
    說明如何讓神經網路學會強化學習中的決策策略。本章介紹並用Keras與OpenAI gym環境實做了四種方法:REINFORCE法、具基準的REINFORCE法、動作-評價法與優勢動作-評價法(A2C)。本章的範例說明了如何在連續型動作空間中執行策略梯度方法。
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    Rowel Atienza

      Dado and Maria Banatao學院的人工智慧講座教授。自從畢業於菲律賓大學之後,Rowel就深深著迷於各種智慧型機器人。Rowel的研究領域著眼於AI與電腦視覺。他的夢想是打造一台可以感知、理解與推理的機器。
    More

    Details

    Released
    2020/04/30
    Language
    Traditional Chinese
    File format
    PDF: Fit in large screen
    Provide Adobe DRM
    Provide PDF
    Offer DRM free license
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    ID
    204460
    ISBN
    9789865025076

    網路

    AI

    Keras

    深度學習

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