TensorFlow Machine Learning Cookbook

TensorFlow Machine Learning Cookbook

By : Nick McClure

Buy this Book

TensorFlow Machine Learning Cookbook

By: Nick McClure

Buy this Book

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.

TensorFlow Machine Learning Cookbook

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

Free Chapter

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

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

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

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

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

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

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

Convolutional Neural Networks

Introduction

Implementing a Simpler CNN

Implementing an Advanced CNN

Retraining Existing CNNs models

Applying Stylenet/Neural-Style

Implementing DeepDream

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

Taking TensorFlow to Production

Introduction

Implementing unit tests

Using Multiple Executors

Parallelizing TensorFlow

Taking TensorFlow to Production

Productionalizing TensorFlow – An Example

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

Index

Customer Reviews

5 star

4 star

3 star

2 star

1 star

Implementing a Non-Linear SVM

For this recipe, we will apply a non-linear kernel to split a dataset.

Getting ready

In this section, we will implement the preceding Gaussian kernel SVM on real data. We will load the iris data set and create a classifier for I. setosa (versus non-setosa). We will see the effect of various gamma values on the classification.

How to do it…

We first load the necessary libraries, which includes the scikit learn datasets so that we can load the iris data. Then we will start a graph session. Use the following code:
```
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from sklearn import datasets
sess = tf.Session()
```

Next we will load the iris data, extract the sepal length and petal width, and separated the x and y values for each class (for plotting purposes later) , as follows:

iris = datasets.load_iris()
x_vals = np.array([[x[0], x[3]] for x in iris.data])
y_vals = np.array([1 if y==0 else -1 for y in iris.target])
class1_x = [x[0] for i,x in enumerate...

TensorFlow Machine Learning Cookbook

By : Nick McClure

TensorFlow Machine Learning Cookbook

By: Nick McClure

Overview of this book

Related Content you might be interested in

Current Title:

TensorFlow Machine Learning Cookbook

Machine Learning Using TensorFlow Cookbook

Predictive Analytics with TensorFlow

Deep Learning with TensorFlow

Implementing a Non-Linear SVM

Getting ready

How to do it…