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[资料] 一步一步教你使用 TensorFlow建立机器学习和深度学习网络

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发表于 2018-5-11 10:43:33 | 显示全部楼层 |阅读模式

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一步一步教你使用 TensorFlow建立机器学习和深度学习网络包含代码和例子程序

Table of Contents
Building Machine Learning Projects with TensorFlow
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
1. Exploring and Transforming Data
TensorFlow's main data structure - tensors
Tensor properties - ranks, shapes, and types
Tensor rank
Tensor shape
Tensor data types
Creating new tensors
From numpy to tensors and vice versa
Useful method
Getting things done - interacting with TensorFlow
Handling the computing workflow - TensorFlow's data flow graph
Computation graph building
Useful operation object methods
Feeding
Variables
Variable initialization
Saving data flow graphs
Graph serialization language - protocol buffers
Useful methods
Example graph building
Running our programs - Sessions
Basic tensor methods
Simple matrix operations
Reduction
Tensor segmentation
Sequences
Tensor shape transformations
Tensor slicing and joining
Dataflow structure and results visualization - TensorBoard
Command line use
How TensorBoard works
Adding Summary nodes
Common Summary operations
Special Summary functions
Interacting with TensorBoard's GUI
Reading information from disk
Tabulated formats - CSV
The Iris dataset
Reading image data
Loading and processing the images
Reading from the standard TensorFlow format
Summary
2. Clustering
Learning from data - unsupervised learning
Clustering
k-means
Mechanics of k-means
Algorithm iteration criterion
k-means algorithm breakdown
Pros and cons of k-means
k-nearest neighbors
Mechanics of k-nearest neighbors
Pros and cons of k-nn
Practical examples for Useful libraries
matplotlib plotting library
Sample synthetic data plotting
scikit-learn dataset module
About the scikit-learn library
Synthetic dataset types
Blobs dataset
Employed method
Circle dataset
Employed method
Moon dataset
Project 1 - k-means clustering on synthetic datasets
Dataset description and loading
Generating the dataset
Model architecture
Loss function description and optimizer loop
Stop condition
Results description
Full source code
k-means on circle synthetic data
Project 2 - nearest neighbor on synthetic datasets
Dataset generation
Model architecture
Loss function description
Stop condition
Results description
Full source code
Summary
3. Linear Regression
Univariate linear modelling function
Sample data generation
Determination of the cost function
Least squares
Minimizing the cost function
General minima for least squares
Iterative methods - gradient descent
Example section
Optimizer methods in TensorFlow - the train module
The tf.train.Optimizer class
Other Optimizer instance types
Example 1 - univariate linear regression
Dataset description
Model architecture
Cost function description and Optimizer loop
Stop condition
Results description
Reviewing results with TensorBoard
Full source code
Example - multivariate linear regression Building Machine Learning Projects with TensorFlow-Packt Publishing (2016).pdf (7.89 MB, 下载次数: 357 )
发表于 2018-5-11 21:09:23 | 显示全部楼层
shafa   , thx for sharing
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发表于 2018-5-12 14:24:18 | 显示全部楼层
好东西,谢谢楼主
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发表于 2018-5-13 01:30:01 | 显示全部楼层
thks very much !!!!!
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发表于 2018-5-14 11:44:42 | 显示全部楼层
thanks a lot
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发表于 2018-5-16 01:15:59 | 显示全部楼层
谢谢楼主
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发表于 2018-5-16 12:08:41 | 显示全部楼层
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发表于 2018-5-17 08:39:05 | 显示全部楼层
谢谢您的分享!!
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发表于 2018-5-17 14:04:41 | 显示全部楼层
gooood
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发表于 2018-5-18 16:52:16 | 显示全部楼层
good document, thank you for your sharing..
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