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

Imbalanced Datasets


Imbalanced datasets are a distinct case for classification problems where the class distribution varies between the classes. In such datasets, one class is overwhelmingly dominant. In other words, the null accuracy of an imbalanced dataset is very high. Consider an example of credit card fraud. If we have a dataset of credit card transactions, then we will find that, of all the transactions, a very miniscule number of transactions were fraudulent and the majority of transactions were normal transactions. If 1 represents a fraudulent transaction and 0 represents a normal transaction, then there will be many 0s and hardly any 1s. The null accuracy of the dataset may be more than 99%. This means the majority class (in this case, 0) is overwhelmingly greater than the minority class (in this case, 1). Such sets are imbalanced datasets. The following figure shows a generalized scatter plot of an imbalanced dataset, where the stars represent the minority class and the circles...