Book Image

Applied Deep Learning with Keras

By : Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme
Book Image

Applied Deep Learning with Keras

By: Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme

Overview of this book

Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code. Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model. By the end of this book, you will have the skills you need to use Keras when building high-level deep neural networks.
Table of Contents (12 chapters)
Applied Deep Learning with Keras
Preface
Preface

Introduction


In this chapter, you will learn about cross-validation, a resampling technique that leads to a very accurate and robust estimation of a model's performance in comparison to the model evaluation approaches discussed in the previous chapters. This chapter starts with an in-depth discussion about why we need to use cross-validation for model evaluation, the underlying basics of cross-validation, its variations, and a comparison between them. Next, we will move on to implementing cross-validation on Keras deep learning models. We will also focus on how to use Keras wrappers with scikit-learn to allow Keras models to be treated as estimators in a scikit-learn workflow. You will then learn how to implement cross-validation in scikit-learn, and finally bring it all together and perform cross-validation using scikit-learn on Keras deep learning models. Lastly, you will learn about using cross-validation to perform more than just model evaluation. You will learn how a cross-validation...