Book Image

TensorFlow Machine Learning Cookbook - Second Edition

By : Nick McClure
Book Image

TensorFlow Machine Learning Cookbook - Second Edition

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 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.

Related Content you might be interested in

Current Title:

Small book image
TensorFlow Machine Learning Cookbook - Second Edition
Nick McClure
Aug, 2018 | 422 pages
Small book image
Predictive Analytics with TensorFlow
Md Rezaul Karim
Nov, 2017 | 522 pages
Small book image
Deep Learning with TensorFlow
Md Rezaul Karim | Giancarlo Zaccone
Mar, 2018 | 484 pages
Small book image
Neural Network Programming with Tensorflow
Manpreet Singh Ghotra | Rajdeep Dua
Nov, 2017 | 274 pages
Table of Contents (13 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Sections
Get in touch
Free Chapter
1
Getting Started with TensorFlow
Getting Started with TensorFlow
Introduction
How TensorFlow works
Declaring variables and tensors
Using placeholders and variables
Working with matrices
Declaring operations
Implementing activation functions
Working with data sources
Additional resources
2
The TensorFlow Way
The TensorFlow Way
Introduction
Operations in a computational graph
Layering nested operations
Working with multiple layers
Implementing loss functions
Implementing backpropagation
Working with batch and stochastic training
Combining everything together
Evaluating models
3
Linear Regression
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
Support Vector Machines
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
Nearest-Neighbor Methods
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
Neural Networks
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
Natural Language Processing
Natural Language Processing
Introduction
Working with bag-of-words embeddings
Implementing TF-IDF
Working with Skip-Gram embeddings
Working with CBOW embeddings
Making predictions with word2vec
Using doc2vec for sentiment analysis
8
Convolutional Neural Networks
Convolutional Neural Networks
Introduction
Implementing a simple CNN
Implementing an advanced CNN
Retraining existing CNN models
Applying stylenet and the neural-style project
Implementing DeepDream
9
Recurrent Neural Networks
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
Taking TensorFlow to Production
Taking TensorFlow to Production
Introduction
Implementing unit tests
Using multiple executors
Parallelizing TensorFlow
Taking TensorFlow to production
An example of productionalizing TensorFlow
Using TensorFlow Serving
11
More with TensorFlow
More with TensorFlow
Introduction
Visualizing graphs in TensorBoard
Working with a genetic algorithm
Clustering using k-means
Solving a system of ordinary differential equations
Using a random forest
Using TensorFlow with Keras
12
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Using a random forest

Random forest algorithms are built on aggregating decision trees on randomly selected observations and/or randomly selected features. We will not cover how decision trees are trained, but will show that there are types of random forests that can be trained using gradient boosting, which TensorFlow can calculate for us.

Getting ready

Tree based algorithms are traditionally non-smooth, as they are based on partitioning the data to minimize the variance in the target outputs. Non-smooth methods do not lend themselves well to gradient based methods. TensorFlow relies on the fact that the functions used in the model are smooth and that it automatically calculates how to change the model parameters to minimize...

Previous Section
End of Section 7
Next Section