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TensorFlow Machine Learning Cookbook

TensorFlow Machine Learning Cookbook

By : Nick McClure
3.7 (18)
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TensorFlow Machine Learning Cookbook

TensorFlow Machine Learning Cookbook

3.7 (18)
By: Nick McClure

Overview of this book

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.
Table of Contents (13 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Sections
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Getting Started with TensorFlow
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
2
2. The TensorFlow Way
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
3
3. Linear Regression
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 Elastic Net Regression
Implementing Logistic Regression
4
4. Support Vector Machines
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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
5
5. Nearest Neighbor Methods
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
6
6. Neural Networks
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
7
7. Natural Language Processing
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
8
8. Convolutional Neural Networks
8. Convolutional Neural Networks
Introduction
Implementing a Simpler CNN
Implementing an Advanced CNN
Retraining Existing CNNs models
Applying Stylenet/Neural-Style
Implementing DeepDream
9
9. Recurrent Neural Networks
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
10
10. Taking TensorFlow to Production
10. Taking TensorFlow to Production
Introduction
Implementing unit tests
Using Multiple Executors
Parallelizing TensorFlow
Taking TensorFlow to Production
Productionalizing TensorFlow – An Example
11
11. More with TensorFlow
11. More with TensorFlow
Introduction
Visualizing graphs in Tensorboard
There's more…
Working with a Genetic Algorithm
Clustering Using K-Means
Solving a System of ODEs
12
Index
Index

Working with Kernels in TensorFlow

The prior SVMs worked with linear separable data. If we would like to separate non-linear data, we can change how we project the linear separator onto the data. This is done by changing the kernel in the SVM loss function. In this chapter, we introduce how to changer kernels and separate non-linear separable data.

Getting ready

In this recipe, we will motivate the usage of kernels in support vector machines. In the linear SVM section, we solved the soft margin with a specific loss function. A different approach to this method is to solve what is called the dual of the optimization problem. It can be shown that the dual for the linear SVM problem is given by the following formula:

Getting ready

Where:

Getting ready

Here, the variable in the model will be the b vector. Ideally, this vector will be quite sparse, only taking on values near 1 and -1 for the corresponding support vectors of our dataset. Our data point vectors are indicated by Getting ready and our targets (1 or -1) are represented by Getting ready...

End of Section 5
Next Section
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