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

Given a passage of text and a question related to that text, the idea of Question Answering (QA) is to identify the subset of the passage that answers the question. It is one of many tasks where Transformer architectures have been applied successfully. The Transformers library has a number of pretrained models for QA that can be applied even in the absence of a dataset to finetune on (a form of zero-shot learning).

However, different models might fail at different examples and it might be useful to examine the reasons. In this section, we'll demonstrate the TensorFlow 2.0 GradientTape functionality: it allows us to record operations on a set of variables we want to perform automatic differentiation on. To explain the model's output on a given input, we can:

  • One-hot encode the input – unlike integer tokens (typically used in this context), a one-hot-encoding representation is differentiable
  • Instantiate GradientTape...