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深度學習
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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