eBook - ePub
TensorFlow Machine Learning Cookbook
Nick McClure
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eBook - ePub
TensorFlow Machine Learning Cookbook
Nick McClure
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Información del libro
Explore machine learning concepts using the latest numerical computing library — TensorFlow — with the help of this comprehensive cookbook
About This Book
- Your quick guide to implementing TensorFlow in your day-to-day machine learning activities
- Learn advanced techniques that bring more accuracy and speed to machine learning
- Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow
Who This Book Is For
This book is ideal for data scientists who are familiar with C++ or Python and perform machine learning activities on a day-to-day basis. Intermediate and advanced machine learning implementers who need a quick guide they can easily navigate will find it useful.
What You Will Learn
- Become familiar with the basics of the TensorFlow machine learning library
- Get to know Linear Regression techniques with TensorFlow
- Learn SVMs with hands-on recipes
- Implement neural networks and improve predictions
- Apply NLP and sentiment analysis to your data
- Master CNN and RNN through practical recipes
- Take TensorFlow into production
In Detail
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.
Style and approach
This book takes a recipe-based approach where every topic is explicated with the help of a real-world example.
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Información
TensorFlow Machine Learning Cookbook
Table of Contents
TensorFlow Machine Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
eBooks, discount offers, and more
Why Subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Sections
Getting ready
How to do it…
How it works…
There's more…
See also
Conventions
Reader feedback
Customer support
Downloading the example code
Piracy
Questions
1. Getting Started with TensorFlow
Introduction
How TensorFlow Works
Getting ready
How to do it…
How it works…
See also
Declaring Tensors
Getting ready
How to do it…
How it works…
There's more…
Using Placeholders and Variables
Getting ready
How to do it…
How it works…
There's more…
Working with Matrices
Getting ready
How to do it…
How it works…
Declaring Operations
Getting ready
How to do it…
How it works…
There's more…
Implementing Activation Functions
Getting ready
How to do it…
How it works…
There's more…
Working with Data Sources
Getting ready
How to do it…
How it works…
See also
Additional Resources
Getting ready
How to do it…
See also
2. The TensorFlow Way
Introduction
Operations in a Computational Graph
Getting ready
How to do it…
How it works…
Layering Nested Operations
Getting ready
How to do it…
How it works…
There's more…
Working with Multiple Layers
Getting ready
How to do it…
How it works…
Implementing Loss Functions
Getting ready
How to do it…
How it works…
There's more…
Implementing Back Propagation
Getting ready
How to do it…
How it works…
There's more…
See also
Working with Batch and Stochastic Training
Getting ready
How to do it…
How it works…
There's more…
Combining Everything Together
Getting ready
How to do it…
How it works…
There's more…
See also
Evaluating Models
Getting ready
How to do it…
How it works…
3. Linear Regression
Introduction
Using the Matrix Inverse Method
Getting ready
How to do it…
How it works…
Implementing a Decomposition Method
Getting ready
How to do it…
How it works…
Learning The TensorFlow Way of Linear Regression
Getting ready
How to do it…
How it works…
Understanding Loss Functions in Linear Regression
Getting ready
How to do it…
How it works…
There's more…
Implementing Deming regression
Getting ready
How to do it…
How it works…
Implementing Lasso and Ridge Regression
Getting ready
How to do it…
How it works…
There's' more…
Implementing Elastic Net Regression
Getting ready
How to do it…
How it works…
Implementing Logistic Regression
Getting ready
How to do it…
How it works…
4. Support Vector Machines
Introduction
Working with a Linear SVM
Getting ready
How to do it…
How it works…
Reduction to Linear Regression
Getting ready
How to do it…
How it works…
Working with Kernels in TensorFlow
Getting ready
How to do it…
How it works…
There's more…
Implementing a Non-Linear SVM
Getting ready
How to do it…
How it works…
Implementing a Multi-Class SVM
Getting ready
How to do it…
How it works…
5. Nearest Neighbor Methods
Introduction
Working with Nearest Neighbors
Getting ready
How to do it…
How it works…
There's more…
Working with Text-Based Distances
Getting ready
How to do it…
How it works…
There's more…
Computing with Mixed Distance Functions
Getting ready
How to do it…
How it works…
There's more…
Using an Address Matching Example
Getting ready
How to do it…
How it works…
Using Nearest Neighbors for Image Recognition
Getting ready
How to do it…
How it works…
There's more…
6. Neural Networks
Introduction
Implementing Operational Gates
Getting ready
How to do it…
How it works…
Working with Gates and Activation Functions
Getting ready
How to do it…
How it works…
There's more…
Implementing a One-Layer Neural Network
Getting ready
How to do it…
How it works…
There's more…
Implementing Different Layers
Getting ready
How to do it…
How it works…
Using a Multilayer Neural Network
Getting ready
How to do it…
How it works…
Improving the Predictions of Linear Models
Getting ready
How to do it
How it works…
Learning to Play Tic Tac Toe
Getting ready
How to do it…
How it works…
7. Natural Language Processing
Introduction
Working with bag of words
Getting ready
How to do it…
How it works…
There's more…
Implementing TF-IDF
Getting ready
How to do it…
How it works…
There's more…
Working with Skip-gram Embeddings
Getting ready
How to do it…
How it works…
There's more…
Working with CBOW Embeddings
Getting ready
How to do it…
How it works…
There's more…
Making Predictions with Word2vec
Getting ready
How to do it…
How it works…
There's more…
Using Doc2vec for Sentiment Analysis
Getting ready
How to do it…
How it works…
8. Convolutional Neural Networks
Introduction
Implementing a Simpler CNN
Getting ready
How to do it…
How it works…
There's more…
See also
Implementing an Advanced CNN
Getting ready
How to do it…
How it works…
See also
Retraining Existing CNNs models
Getting ready
How to do it…
How it works…
See also
Applying Stylenet/Neural-Style
Getting ready
How to do it…
How it works…
See also
Implementing DeepDream
Getting ready
How to do it…
There's more…
See also
9. Recurrent Neural Networks
Introduction
Implementing RNN for Spam Prediction
Getting ready
How to do it…
How it works…
There's more…
Implementing an LSTM Model
Getting ready
How to do it…
How it works…
There's more…
Stacking multiple LSTM Layers
Getting ready
How to do it…
How it works…
Creating Sequence-to-Sequence Models
Getting ready
How to do it…
How it works…
There's more…
Training a Siamese Similarity Measure
Getting ready
How to do it…
There's more…
10. Taking TensorFlow to Production
Introduction
Implementing unit tests
Getting ready
How it works…
Using Multiple Executors
Getting ready
How to do it…
How it works…
There's more…
Parallelizing TensorFlow
Getting ready
How to do it…
How it works…
Taking TensorFlow to Production
Getting ready
How to do it…
How it works…
Productionalizing TensorFlow – An Example
Getting ready
How to do it…
How it works…
11. More with TensorFlow
Introduction
Visualizing graphs in Tensorboard
Getting ready
How to do it…
There's more…
Working with a Genetic Algorithm
Getting ready
How to do it…
How it works…
There's more…
Clustering Using K-Means
Getting ready
How to do it…
There's more…
Solving a System of ODEs
Getting ready
How to do it…
How it works…
See also
Index
TensorFlow Machine Learning Cookbook
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