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內容簡介

　　TensorFlow是一個用於機器智能的開源軟件庫。書中的每一個實例都會教你如何使用TensorFlow應對復雜的數據計算，使你比以前更深入的探究數據，加深對於數據的認識。這些實例涵蓋瞭模型訓練、模型評估、情感分析、迴歸分析、聚類分析、人工神經網絡以及深度學習，每一個都用到瞭Google的機器學習庫TensorFlow。
　　《TensorFlow機器學習攻略（英文 影印版）》首先介紹瞭TensorFlow的基礎知識，其中包括變量、矩陣以及各種數據源。然後你將使用TensorFlow來學習綫性迴歸技術。剩下的部分涵蓋瞭其他一些重要的高級概念，如神經網絡、CNN、RNN和NLP。
　　當你熟悉並適應瞭TensorFlow的生態係統，《TensorFlow機器學習攻略（英文 影印版）》最後一章將為你展示如何將其應用到産品中。

內頁插圖

目錄

Preface
Chapter 1： GettingStarted with TensorFlow
Introduction
How TensorFIow 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 TensorFIow Way of Linear Regression
Understanding Loss Functions in Linear Regression
Implementing Deming regression
Implementing Lasso and Ridge Regression
Implementing Elastic Net Regression
Implementing Logistic Regression

Chapter 4： Support Vector Machines
Introduction
Working with a Linear SVM
Reduction to Linear Regression
Working with Kernels in TensorFIow
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/NeuraI-Style
Implementing DeepDream

Chapter 9： Recurrent Neural Networks
Introduction
Implementing RNN for Spam Prediction
Implementing an LSTM Model
Stacking multiple LSTM Layers
Creating Sequence-to-Sequence Models
Training a Siamese Similarity Measure

Chapter 10： Taking TensorFIow to Production
Introduction
Implementing unit tests
Using Multiple Executors
Parallelizing TensorFIow
Taking TensorFIow to Production
Productionalizing TensorFIow - An Example

Chapter 11： More with TensorFIow
Introduction
Visualizing graphs in Tensorboard
There's more．．．
Working with a Genetic Algorithm
Clustering Using K-Means
Solving a System of ODEs
Index
TensorFlow機器學習攻略（英文 影印版） 下載 mobi epub pdf txt 電子書

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