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

Learning the TensorFlow way of linear regression

The statistical approach in linear regression, using matrices and decomposition methods on data, is very powerful. In any event TensorFlow has another means to solve for the coefficients of a slope and an intercept in a regression problem. TensorFlow can achieve a result in such problems iteratively, that is, gradually learning the best linear regression parameters that will minimize the loss, as we have seen in the recipes in previous chapters.

The interesting fact is that you actually don't have to write all the code from scratch when dealing with a regression problem in TensorFlow: Estimators and Keras can assist you in doing that. Estimators are to be found in tf.estimator, a high-level API in TensorFlow.

Estimators were introduced in TensorFlow 1.3 (see https://github.com/tensorflow/tensorflow/releases/tag/v1.3.0-rc2) as ''canned Estimators'', pre-made specific procedures (such as regression...