Moolayil - Learn Keras for deep neural networks: a fast-track approach to modern deep learning with Python

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-4239-1 . For more detailed information, please visit http://www.apress.com/source-code .

This book is intended to gear the readers with a superfast crash course on deep learning. Readers are expected to have basic programming skills in any modern-day language; Python experience would be great, but is not necessary. Given the limitations on the size and depth of the subject we can cover, this short guide is intended to equip you as a beginner with sound understanding of the topic, including tangible practical experience in model development that will help develop a foundation in the deep learning domain.

This guide is not recommended if you are already above the beginner level and are keen to explore advanced topics in deep learning like computer vision, speech recognition, and so on. The topics of CNN, RNN, and modern unsupervised learning algorithms are beyond the scope of this guide. We provide only a brief introduction to these to keep the readers aware contextually about more advanced topics and also provide recommended sources to explore these topics in more detail.

The book is focused on a fast-paced approach to exploring practical deep learning concepts with math and programming-friendly abstractions. You will learn to design, develop, train, validate, and deploy deep neural networks using the industrys favorite Keras framework. You will also learn about the best practices for debugging and validating deep learning models and briefly learn about deploying and integrating deep learning as a service into a larger software service or product. Finally, with the experience gained in building deep learning models with Keras, you will also be able to extend the same principles into other popular frameworks.

The primary target audience for this book consists of software engineers and data engineers keen on exploring deep learning for a career move or an upcoming enterprise tech project. We understand the time crunch you may be under and the pain of assimilating new content to get started with the least amount of friction. Additionally, this book is for data science enthusiasts and academic and research professionals exploring deep learning as a tool for research and experiments.

We follow the lazy programming approach in this guide. We start with a basic introduction, and then cater to the required context incrementally at each step. We discuss how each building block functions in a lucid way and then learn about the abstractions available to implement them.

The book is organized into three sections with two chapters each.

Section 1 equips you with all the necessary gear to get started on the fast-track ride into deep learning. Chapter will help you get started with a hands-on exercise in Keras, understanding the basic building blocks of deep learning and developing the first basic DNN.

Section 2 embraces the fundamentals of deep learning in simple, lucid language while abstracting the math and complexities of model training and validation with the least amount of code without compromising on flexibility, scale, and the required sophistication. Chapter delves into the craft of validating deep neural networks (i.e., measuring performance and understanding the shortcomings and the means to circumvent them).

Section 3 concludes the book with topics on further model improvement and the path forward. Chapter the conclusiondiscusses the path ahead for the reader to further hone his or her skills in deep learning and discusses a few areas of active development and research in deep learning.

At the end of this crash course, the reader will have gained a thorough understanding of the deep learning principles within the shortest possible time frame and will have obtained practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.

I would like to thank my parents, my brother Tijo, and my sister Josna for their constant support and love.