In this post we will train an autoencoder to detect credit card fraud. We will also demonstrate how to train Keras models in the cloud using CloudML. The basis of our model will be the Kaggle Credit Card Fraud Detection dataset.
Table of Contents Introduction to Autoencoders What Are Autoencoders? How Autoencoders Achieve High-Quality Reconstructions? Revisiting the Story Types of Autoencoder Vanilla Autoencoder Convolutional Autoencoder (CAE) Denoising Autoencoder Sparse Autoencoder Variational Autoencoder (VAE) Sequence-to-Sequence Autoencoder What Are the Applications of Autoencoders?…
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Machine learning advancements lead to new ways to train models, as well as deceive them. This article discusses ways to train and defend against attacks.
With the rapid advance in NLP models we have outpaced out ability to measure just how good they are at human level language tasks. We need better NLP datasets now more than ever to both evaluate how good these models are and to be able to tweak them for out own business domains.
What is Attention, and why is it used in state-of-the-art models? This article discusses the types of Attention and walks you through their implementations.
We will cover often-overlooked concepts vital to NLP, such as Byte Pair Encoding, and discuss how understanding them leads to better models.
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