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

Open-domain question answering

Question-answering (QA) systems aim to emulate the human process of searching for information online, with machine learning methods employed to improve the accuracy of the provided answers. In this recipe, we will demonstrate how to use RNNs to predict long and short responses to questions about Wikipedia articles. We will use the Google Natural Questions dataset, along with which an excellent visualization helpful for understanding the idea behind QA can be found at https://ai.google.com/research/NaturalQuestions/visualization.

The basic idea can be summarized as follows: for each article-question pair, you must predict/select long- and short-form answers to the question drawn directly from the article:

  • A long answer would be a longer section of text that answers the question—several sentences or a paragraph.
  • A short answer might be a sentence or phrase, or even in some cases a simple YES/NO. The short answers are always...