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Deep Learning with Keras 2

by Jerry Kurata

Deep Learning lies at the heart of many leading machine learning and artificial intelligence applications. This course, Deep Learning with Keras, shows you how to use Keras to quickly create powerful deep neural networks.

What you'll learn

There has been a revolution in artificial intelligence (AI) and machine learning, and deep learning-based solutions are leading the charge. Implementing these solutions can be tedious to create and require you to write many lines of complex code. Keras is a library that makes it much easier for you to create these deep learning solutions. In a few lines of code, you can create a model that could require hundreds of lines of conventional code.

This course, Deep Learning with Keras, will get you up to speed with both the theory and practice of using Keras to implement deep neural networks.

First, you will dive deep into learning how Keras implements various layers of neurons quickly and easily, with each layer defining the specific functionality needed to implement parts of your solution.

Next, you will discover how to use Keras’ various methods for interconnecting these layers to form the structure of your deep neural networks. Finally, you will learn how you use Keras to implement several state-of-the-art neural networks, such as the widely used Convolutional and Recurrent Neural Networks, to make these concepts come to life.

By the end of this course, you will gain the skills and experience required to effectively create deep neural networks through the course’s combination of lecture and hands-on coding.

About the author

Jerry has Bachelor of Science degrees in Geology and Physics. His plans to work in the oil exploration industry were sidetracked when he discovered he preferred to work with computers on simulation and data processing, instead of reading mud and core samples in the North Sea. His love of computers and tech resulted in him spending many additional hours working on computers while getting his Master’s degree in Computer Science. His current areas of interests include Machine Learning, Big Data,... more

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