Learn what are AutoEncoders, how they work, their usage, and finally implement Autoencoders for anomaly detection in perform fraud detection using Autoencoders in TensorFlow
Learn what are AutoEncoders, how they work, their usage, and finally implement Autoencoders for anomaly detection.
AutoEncoder is a generative unsupervised deep learning algorithm used for reconstructing high-dimensional input data using a neural network with a narrow bottleneck layer in the middle which contains the latent representation of the input data.
Autoencoder consists of an Encoder and a Decoder.
The Autoencoder accepts high-dimensional input data, compress it down to the latent-space representation in the bottleneck hidden layer; the Decoder takes the latent representation of the data as an input to reconstruct the original input data.
Read about different types of Autoencoder **[here](https://medium.com/datadriveninvestor/deep-learning-different-types-of-autoencoders-41d4fa5f7570).**
In this post, we'll learn top 30 Python Tips and Tricks for Beginners
Applying Anomaly Detection: Credit card fraud can be classified as an anomaly and using autoencoders implemented in Keras it is possible to detect fraud.
Here, we will discuss how deep learning in python works for various applications, including neural networks and computer vision. A new and useful knowledge. It's a pity if you ignore it.
Inexture's Deep learning Development Services helps companies to develop Data driven products and solutions. Hire our deep learning developers today to build application that learn and adapt with time.
Reviewing challenges, methods and opportunities in deep anomaly detection. This post summarizes a comprehensive survey paper on deep learning for anomaly detection .