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

Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are the primary modern approach for modeling data that is sequential in nature. The word "recurrent" in the name of the architecture class refers to the fact that the output of the current step becomes the input to the next one (and potentially further ones as well). At each element in the sequence, the model considers both the current input and what it "remembers" about the preceding elements.

Natural Language Processing (NLP) tasks are one of the primary areas of application for RNNs: if you are reading through this very sentence, you are picking up the context of each word from the words that came before it. NLP models based on RNNs can build on this approach to achieve generative tasks, such as novel text creation, as well as predictive ones such as sentiment classification or machine translation.

In this chapter, we'll cover the following topics:

  • Text generation
  • ...