Book Image

Machine Learning Using TensorFlow Cookbook

By : Luca Massaron, Alexia Audevart, Konrad Banachewicz
Book Image

Machine Learning Using TensorFlow Cookbook

By: Luca Massaron, Alexia Audevart, Konrad Banachewicz

Overview of this book

The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow. This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression. Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems. With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios.
Table of Contents (15 chapters)
5
Boosted Trees
11
Reinforcement Learning with TensorFlow and TF-Agents
13
Other Books You May Enjoy
14
Index

Resorting to non-linear solutions

Linear models are approachable and interpretable, given the one-to-one relation between feature columns and regression coefficients. Sometimes, anyway, you may want to try non-linear solutions in order to check whether models that are more complex can model your data better and solve your prediction problem in a more expert manner. Support Vector Machines (SVMs) are an algorithm that rivaled neural networks for a long time and they are still a viable option thanks to recent developments in terms of random features for large-scale kernel machines (Rahimi, Ali; Recht, Benjamin. Random features for large-scale kernel machines. In: Advances in neural information processing systems. 2008. pp. 1177-1184). In this recipe, we demonstrate how to leverage Keras and obtain a non-linear solution to a classification problem.

Getting ready

We will still be using functions from the previous recipes, including define_feature_columns_layers and make_input_fn...