TensorFlow机器学习攻略
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作者Nick Mcclure著
出版社东南大学出版社
ISBN9787564173661
出版时间2017-10
装帧平装
开本其他
定价84元
货号9079111
上书时间2025-01-10
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作者简介
NickMcClure，资深数据科学家，目前就职于美国西雅图PayScale公司。曾经在凯撒娱乐集团工作。他在蒙大拿大学和圣本尼迪克与圣约翰大学学院的应用数学专业获得学位。他热衷于数据分析、机器学习和人工智能。Nick有时会把想法写成博客（http://fromdata.org/）或者推特（@nfmcclure）。
目录
Preface
Chapter 1： Getting Started with TensorFlow
Introduction
How TensorFlow Works
Declaring Tensors
Using Placeholders and Variables
Working with Matrices
Declaring Operations
Implementing Activation Functions
Working with Data Sources
Additional Resources
Chapter 2：The TensorFlow Way
Introduction
Operations In a Computational Graph
Layering Nested Operations
Working with Multiple Layers
Implementing Loss Functions
Implementing Back Propagation
Working with Batch and Stochastic Training
Combining Everything Together
Evaluating Models
Chapter 3： Linear Regression
Introduction
Using the Matrix Inverse Method
Implementing a Decomposition Method
Learning The TensorFlow Way of Linear Regression
Understanding Loss Functions In Linear Regression
Implementing Deming regression
Implementing Lasso and Ridge Regression
Implementing Elastlc Net Regression
Implementing Logistic Regression
Chapter 4： Support Vector Machines
Introduction
Working with a Linear SVM
Reduction to Linear Regression
Working with Kernels in TensorFlow
Implementing a Non—Linear SVM
Implementing a Multi—Class SVM
Chapter 5： Nearest Neighbor Methods
Introduction
Working with Nearest Neighbors
Working with Text—Based Distances
Computing with Mixed Distance Functions
Using an Address Matching Example
Using Nearest Neighbors for Image Recognition
Chapter 6： Neural Networks
Introduction
Implementing Operational Gates
Working with Gates and Activation Functions
Implementing a One—Layer Neural Network
Implementing Different Layers
Using a Multilayer Neural Network
Improving the Predictions of Linear Models
Learning to Play Tic Tac Toe
Chapter 7： Natural Language Processing
Introduction
Working with bag of words
Implementing TF—IDF
Working with Skip—gram Embeddings
Working with CBOW Embeddings
Making Predictions with Word2vec
Using Doc2vec for Sentiment Analysis
Chapter 8： Convolutional Neural Networks
Introduction
Implementing a Simpler CNN
Implementing an Advanced CNN
Retraining Existing CNNs models
Applying Stylenet／Neural—Style
Implementing DeepDream
Chapter 9： Recurrent Neural Networks
Introduction
Implementing RNN for Spam Prediction
Implementing an LSTM Model
Stacking multiple LSTM Layers
Creatlng Sequence—to—Sequence Models
Training a Siamese Similarity Measure
Chapter 10： Taking TensorFlow to Production
Introduction
Implementing unit tests
Using Multiple Executors
Parallelizing TensorFlow
Taking TensorFlow to Production
Productionalizing TensorFlow—An Example
Chapter 11： More with TensorFlow
Introduction
Visualizing graphs in Tensorboard
Theres more...
Working with a Genetic Algorithm
Clustering Using K—Means
Solving a System of ODEs
Index
内容摘要
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