Skip the theory and get the most out of Tensorflow to build production-ready machine learning models Key Features Exploit the features of Tensorflow to build and deploy machine learning models Train neural networks to tackle real-world problems in Computer Vision and NLP Handy techniques to write production-ready code for your Tensorflow models Book Description 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 allow you to dig deeper and gain more insights into your data than ever before. With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production. By the end of the book, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios. What you will learn Become familiar with the basic features of the TensorFlow library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks to improve predictive modeling Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Implement the gradient boosted random forest to predict housing prices Take TensorFlow into production Who this book is for If you are a data scientist or a machine learning engineer with some knowledge of linear algebra, statistics, and machine learning, this book is for you. If you want to skip the theory and build production-ready machine learning models using Tensorflow without reading pages and pages of material, this book is for you. Some background in Python programming is assumed.
Author(s): Nick McClure
Edition: 2
Year: 2018
Language: English
Pages: 422
Cover
Title Page
Copyright and Credits
Dedication
Packt Upsell
Contributors
Table of Contents
Preface
Chapter 1: Getting Started with TensorFlow
Introduction
How TensorFlow works
Getting ready
How to do it...
How it works...
See also
Declaring variables and 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...
Chapter 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 backpropagation
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...
Chapter 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...
Chapter 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...
Chapter 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...
Chapter 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...
Chapter 7: Natural Language Processing
Introduction
Working with bag-of-words embeddings
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...
Chapter 8: Convolutional Neural Networks
Introduction
Implementing a simple 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 CNN models
Getting ready
How to do it...
How it works...
See also
Applying stylenet and the neural-style project
Getting ready
How to do it...
How it works...
See also
Implementing DeepDream
Getting ready
How to do it...
There's more...
See also
Chapter 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...
Chapter 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...
An example of productionalizing TensorFlow
Getting ready
How to do it...
How it works...
Using TensorFlow Serving
Getting ready
How to do it...
How it works...
There's more...
Chapter 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 ordinary differential equations
Getting ready
How to do it...
How it works...
See also
Using a random forest
Getting ready
How to do it...
How it works...
See also
Using TensorFlow with Keras
Getting ready
How to do it...
How it works...
See also
Other Books You May Enjoy
Index
