Refine Search

New Search

Advanced search

Journal Learn Keras for Deep Neural Networks

-
7 articles
Page of 1
Articles per Page
by
Jojo Moolayil
Learn Keras for Deep Neural Networks; doi:10.1007/978-1-4842-4240-7

The publisher has not yet granted permission to display this abstract.
Jojo Moolayil
Learn Keras for Deep Neural Networks pp 161-176; doi:10.1007/978-1-4842-4240-7_6

The publisher has not yet granted permission to display this abstract.
Jojo Moolayil
Learn Keras for Deep Neural Networks pp 101-135; doi:10.1007/978-1-4842-4240-7_4

Abstract:In Chapter 3, we explored a DL use case for regression. We explored the entire problem-solving approach with a business-forward strategy. We leveraged all our learning from Chapters 1 and 2 in foundational DL and the Keras framework to develop DNNs for a regression use case. In this chapter, we will take our learning one step further and design a network for a classification use case. The approach overall remains the same, but there are a few nuances we need to keep in mind while solving a classification use case. Moreover, we will take our learning in this chapter one step ahead with extensive DNN architectures. Let’s get started.
Jojo Moolayil
Learn Keras for Deep Neural Networks pp 1-16; doi:10.1007/978-1-4842-4240-7_1

Abstract:In this chapter, we will explore the field of deep learning (DL) with a brief introduction and then move to have a look at the popular choices of available frameworks for DL development. We will also take a closer look at the Keras ecosystem to understand why it is special and have a look at a sample code to understand how easy the framework is for developing DL models.
Jojo Moolayil
Learn Keras for Deep Neural Networks pp 137-159; doi:10.1007/978-1-4842-4240-7_5

Abstract:So far in the journey of this book, we have primarily talked about how to develop a DNN for a given use case and looked at a few strategies and rules of thumb to bypass roadblocks we could face in the process. In this chapter, we will discuss the journey onward after developing the initial model by exploring the methods and approaches you need to implement when the model developed doesn’t perform to your expectations. We will discuss regularization and hyperparameter tuning, and toward the end of the chapter, we will also have a high-level view of the process to deploy a model after tuning. However, we won’t actually discuss the implementation specifics of deploying; this will just be an overview offering guidelines to achieve success in the process. Let’s get started.
Jojo Moolayil
Learn Keras for Deep Neural Networks pp 17-52; doi:10.1007/978-1-4842-4240-7_2

Abstract:In this chapter, we will explore the Keras framework and get started with hands-on exercises to learn the basics of Keras along with a bit of Python and the necessary DL topics. A word of caution, given that this a fast-track guide: we will not have the scope to talk in detail about exhaustive topics in DL. Instead, we will start with a simple topic, explore the basic idea behind it, and add references where you can dive deeper for a more foundational understanding of the topic.
Jojo Moolayil
Learn Keras for Deep Neural Networks pp 53-99; doi:10.1007/978-1-4842-4240-7_3

Abstract:In Chapters 1 and 2, we explored the topic of DL and studied how DL evolved from ML to solve an interesting area of problems. We discussed the need for DL frameworks and briefly explored a few popular frameworks available in the market. We then studied why Keras is special and spent some time playing around with its basic building blocks provided to develop DNNs and also understood the intuition behind a DL model holistically. We then put together all our learnings from the practical exercises to develop a baby neural network for the Boston house prices use case.
Page of 1
Articles per Page
by