Advanced Deep Learning with Keras

Introducing Advanced Deep Learning with Keras
Why is Keras the perfect deep
learning library?
Keras [Chollet, François. "Keras (2015)." (2017)] is a popular deep learning library with
over 250,000 developers at the time of writing, a number that is more than doubling
every year. Over 600 contributors actively maintain it. Some of the examples we'll
use in this book have been contributed to the official Keras GitHub repository.
Google's TensorFlow, a popular open source deep learning library, uses Keras as
a high-level API to its library. In the industry, Keras is used by major technology
companies like Google, Netflix, Uber, and NVIDIA. In this chapter, we introduce
how to use Keras Sequential API.
We have chosen Keras as our tool of choice to work within this book because Keras
is a library dedicated to accelerating the implementation of deep learning models.
This makes Keras ideal for when we want to be practical and hands-on, such as
when we're exploring the advanced deep learning concepts in this book. Because
Keras is intertwined with deep learning, it is essential to learn the key concepts
of deep learning before someone can maximize the use of Keras libraries.
All examples in this book can be found on GitHub at the following link:
https://github.com/PacktPublishing/Advanced-Deep-
Learning-with-Keras.
Keras is a deep learning library that enables us to build and train models efficiently.
In the library, layers are connected to one another like pieces of Lego, resulting
in a model that is clean and easy to understand. Model training is straightforward
requiring only data, a number of epochs of training, and metrics to monitor. The
end result is that most deep learning models can be implemented with a significantly
smaller number of lines of code. By using Keras, we'll gain productivity by saving
time in code implementation which can instead be spent on more critical tasks
such as formulating better deep learning algorithms. We're combining Keras with
deep learning, as it offers increased efficiency when introduced with the three deep
learning networks that we will introduce in the following sections of this chapter.
Likewise, Keras is ideal for the rapid implementation of deep learning models, like the
ones that we will be using in this book. Typical models can be built in few lines of
code using the Sequential Model API. However, do not be misled by its simplicity.
Keras can also build more advanced and complex models using its API and Model
and Layer classes which can be customized to satisfy unique requirements. Functional
API supports building graph-like models, layers reuse, and models that are behaving
like Python functions. Meanwhile, Model and Layer classes provide a framework for
implementing uncommon or experimental deep learning models and layers.
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