Book Image

Deep Learning with TensorFlow - Second Edition

By : Giancarlo Zaccone, Md. Rezaul Karim
Book Image

Deep Learning with TensorFlow - Second Edition

By: Giancarlo Zaccone, Md. Rezaul Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
Table of Contents (15 chapters)
Deep Learning with TensorFlow - Second Edition
Contributors
Preface
Other Books You May Enjoy
Index

Fraud analytics with autoencoders


Fraud detection and prevention in financial companies such as banks, insurance companies, and credit unions is an important task. So far, we have seen how, and where, to use Deep Neural Networks (DNNs) and Convolutional Neural Network (CNNs).

Now it's time to use other unsupervised learning algorithm, such as autoencoders. In this section, we will be exploring a dataset of credit card transactions and trying to build an unsupervised machine-learning model that is able to tell whether a particular transaction is fraudulent or genuine.

More specifically, we will use autoencoders to pretrain a classification model and apply anomaly detection techniques to predict possible fraud. Before we start, we need to know the dataset.

Description of the dataset

For this example, we will be using the Credit Card Fraud Detection dataset from Kaggle. The dataset can be downloaded from https://www.kaggle.com/hunk3749/credit-card/data. Since I am using the dataset, it would a...