Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Antonio Gulli, Amita Kapoor, Sujit Pal
Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Antonio Gulli, Amita Kapoor, Sujit Pal

Overview of this book

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
Table of Contents (19 chapters)
17
Other Books You May Enjoy
18
Index

Distributed representations

Distributed representations attempt to capture the meaning of a word by considering its relations with other words in its context. The idea behind the distributed hypothesis is captured in this quote from J. R. Firth, a linguist who first proposed this idea:

"You shall know a word by the company it keeps."

How does this work? By way of example, consider the following pair of sentences:

Paris is the capital of France.

Berlin is the capital of Germany.

Even assuming no knowledge of world geography, the sentence pair implies some sort of relationship between the entities Paris, France, Berlin, and Germany that could be represented as:

"Paris" is to "France" as "Berlin" is to "Germany"

Distributed representations are based on the idea that there exists some transformation such that:

In other words, a distributed embedding space is one where words that are used in similar contexts...