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TensorFlow Machine Learning Cookbook
Nick McClure
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eBook - ePub
TensorFlow Machine Learning Cookbook
Nick McClure
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About This Book
Explore machine learning concepts using the latest numerical computing library ā TensorFlow ā 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 ā 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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Information
TensorFlow Machine Learning Cookbook
Table of Contents
TensorFlow Machine Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
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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 itā¦
How it worksā¦
There's moreā¦
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 itā¦
How it worksā¦
See also
Declaring Tensors
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Using Placeholders and Variables
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Working with Matrices
Getting ready
How to do itā¦
How it worksā¦
Declaring Operations
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Implementing Activation Functions
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Working with Data Sources
Getting ready
How to do itā¦
How it worksā¦
See also
Additional Resources
Getting ready
How to do itā¦
See also
2. The TensorFlow Way
Introduction
Operations in a Computational Graph
Getting ready
How to do itā¦
How it worksā¦
Layering Nested Operations
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Working with Multiple Layers
Getting ready
How to do itā¦
How it worksā¦
Implementing Loss Functions
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Implementing Back Propagation
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
See also
Working with Batch and Stochastic Training
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Combining Everything Together
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
See also
Evaluating Models
Getting ready
How to do itā¦
How it worksā¦
3. Linear Regression
Introduction
Using the Matrix Inverse Method
Getting ready
How to do itā¦
How it worksā¦
Implementing a Decomposition Method
Getting ready
How to do itā¦
How it worksā¦
Learning The TensorFlow Way of Linear Regression
Getting ready
How to do itā¦
How it worksā¦
Understanding Loss Functions in Linear Regression
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Implementing Deming regression
Getting ready
How to do itā¦
How it worksā¦
Implementing Lasso and Ridge Regression
Getting ready
How to do itā¦
How it worksā¦
There's' moreā¦
Implementing Elastic Net Regression
Getting ready
How to do itā¦
How it worksā¦
Implementing Logistic Regression
Getting ready
How to do itā¦
How it worksā¦
4. Support Vector Machines
Introduction
Working with a Linear SVM
Getting ready
How to do itā¦
How it worksā¦
Reduction to Linear Regression
Getting ready
How to do itā¦
How it worksā¦
Working with Kernels in TensorFlow
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Implementing a Non-Linear SVM
Getting ready
How to do itā¦
How it worksā¦
Implementing a Multi-Class SVM
Getting ready
How to do itā¦
How it worksā¦
5. Nearest Neighbor Methods
Introduction
Working with Nearest Neighbors
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Working with Text-Based Distances
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Computing with Mixed Distance Functions
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Using an Address Matching Example
Getting ready
How to do itā¦
How it worksā¦
Using Nearest Neighbors for Image Recognition
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
6. Neural Networks
Introduction
Implementing Operational Gates
Getting ready
How to do itā¦
How it worksā¦
Working with Gates and Activation Functions
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Implementing a One-Layer Neural Network
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Implementing Different Layers
Getting ready
How to do itā¦
How it worksā¦
Using a Multilayer Neural Network
Getting ready
How to do itā¦
How it worksā¦
Improving the Predictions of Linear Models
Getting ready
How to do it
How it worksā¦
Learning to Play Tic Tac Toe
Getting ready
How to do itā¦
How it worksā¦
7. Natural Language Processing
Introduction
Working with bag of words
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Implementing TF-IDF
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Working with Skip-gram Embeddings
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Working with CBOW Embeddings
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Making Predictions with Word2vec
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Using Doc2vec for Sentiment Analysis
Getting ready
How to do itā¦
How it worksā¦
8. Convolutional Neural Networks
Introduction
Implementing a Simpler CNN
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
See also
Implementing an Advanced CNN
Getting ready
How to do itā¦
How it worksā¦
See also
Retraining Existing CNNs models
Getting ready
How to do itā¦
How it worksā¦
See also
Applying Stylenet/Neural-Style
Getting ready
How to do itā¦
How it worksā¦
See also
Implementing DeepDream
Getting ready
How to do itā¦
There's moreā¦
See also
9. Recurrent Neural Networks
Introduction
Implementing RNN for Spam Prediction
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Implementing an LSTM Model
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Stacking multiple LSTM Layers
Getting ready
How to do itā¦
How it worksā¦
Creating Sequence-to-Sequence Models
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Training a Siamese Similarity Measure
Getting ready
How to do itā¦
There's moreā¦
10. Taking TensorFlow to Production
Introduction
Implementing unit tests
Getting ready
How it worksā¦
Using Multiple Executors
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Parallelizing TensorFlow
Getting ready
How to do itā¦
How it worksā¦
Taking TensorFlow to Production
Getting ready
How to do itā¦
How it worksā¦
Productionalizing TensorFlow ā An Example
Getting ready
How to do itā¦
How it worksā¦
11. More with TensorFlow
Introduction
Visualizing graphs in Tensorboard
Getting ready
How to do itā¦
There's moreā¦
Working with a Genetic Algorithm
Getting ready
How to do itā¦
How it worksā¦
There's moreā¦
Clustering Using K-Means
Getting ready
How to do itā¦
There's moreā¦
Solving a System of ODEs
Getting ready
How to do itā¦
How it worksā¦
See also
Index
TensorFlow Machine Learning Cookbook
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