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

Transformers

Transformers are deep learning architectures introduced by Google in 2017 that are designed to process sequential data for downstream tasks such as translation, question answering, or text summarization. In this manner, they aim to solve a similar problem to RNNs discussed in Chapter 9, Recurrent Neural Networks, but Transformers have a significant advantage as they do not require processing the data in order. Among other advantages, this allows a higher degree of parallelization and therefore faster training.

Due to their flexibility, Transformers can be pretrained on large bodies of unlabeled data and then finetuned for other tasks. Two main groups of such pretrained models are Bidirectional Encoder Representations from Transformers (BERTs) and Generative Pretrained Transformers (GPTs).

In this chapter, we will cover the following topics:

  • Text generation
  • Sentiment analysis
  • Text classification: sarcasm detection
  • Question answering...