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TensorFlow深度學習

TensorFlow深度學習

出版社:東南大學出版社出版時間:2019-05-01
開本: 24cm 頁數: 15,458頁
中 圖 價:¥81.0(7.5折) 定價  ¥108.0 登錄后可看到會員價
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TensorFlow深度學習 版權信息

  • ISBN:9787564183264
  • 條形碼:9787564183264 ; 978-7-5641-8326-4
  • 裝幀:一般膠版紙
  • 冊數:暫無
  • 重量:暫無
  • 所屬分類:>

TensorFlow深度學習 本書特色

TensorFlow是谷歌研發(fā)的人工智能學習系統,是一個用于數值計算的開源軟件庫。本書以基礎加實踐相結合的形式,詳細介紹了TensorFlow深度學習算法原理及編程技巧。通讀全書,讀者不僅可以系統了解深度學習的相關知識,還能對使用TensorFlow進行深度學習算法設計的過程有更深入的理解。

TensorFlow深度學習 內容簡介

TensorFlow是谷歌研發(fā)的人工智能學習系統,是一個用于數值計算的開源軟件庫。本書以基礎加實踐相結合的形式,詳細介紹了TensorFlow深度學習算法原理及編程技巧。通讀全書,讀者不僅可以系統了解深度學習的相關知識,還能對使用TensorFlow進行深度學習算法設計的過程有更深入的理解。

TensorFlow深度學習 目錄

Preface Chapter 1: Getting Started with Deep Learning A soft introduction to machine learning Supervised learning Unbalanced data Unsupervised learning Reinforcement learning What is deep learning? Artificial neural networks The biological neurons The artificial neuron How does an ANN learn? ANNs and the backpropagation algorithm Weight optimization Stochastic gradient descent Neural network architectures Deep Neural Networks (DNNs) Multilayer perceptron Deep Belief Networks (DBNs) Convolutional Neural Networks (CNNs) AutoEncoders Recurrent Neural Networks (RNNs) Emergent architectures Deep learning frameworks Summary Chapter 2: A First Look at TensorFlow A general overview of TensorFlow What's new in TensorFlow vl.6? Nvidia GPU support optimized Introducing TensorFlow Lite Eager execution Optimized Accelerated Linear Algebra (XLA) Installing and configuring TensorFlow TensorFlow computational graph TensorFlow code structure Eager execution with TensorFIow Data model in TensorFlow Tensor Rank and shape Data type Variables Fetches Feeds and placeholders Visualizing computations through TensorBoard How does TensorBoard work? Linear regression and beyond Linear regression revisited for a real dataset Summary Chapter 3: Feed-Forward Neural Networks with TensorFIow Feed-forward neural networks (FFNNs) Feed-forward and backpropagation Weights and biases Activation functions Using sigmoid Using tanh Using ReLU Using softmax Implementing a feed-forward neural network Exploring the MNIST dataset Softmax classifier Implementing a multilayer perceptron (MLP) Training an MLP Using MLPs Dataset description Preprocessing A TensorFIow implementation of MLP for client-subscription assessment Chapter 4: Convolutional Neural Networks Chapter 5: Optimizing TensorFIow Autoencoders Chapter 6: Recurrent Neural Networks Chapter 7: Heterogeneous and Distributed Computing Chapter 8: Advanced TensorFIow Programming Chapter 9: Recommendation Systems Using Factorization Machines Chapter 10: Reinforcement Learning Other Books You May Enjoy Index
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