Introduction to AutoEncoder and Variational AutoEncoder (VAE) - Oct 22, 2021.
Autoencoders and their variants are interesting and powerful artificial neural networks used in unsupervised learning scenarios. Learn how autoencoders perform in their different approaches and how to implement with Keras on the instructional data set of the MNIST digits.
Autoencoder, Deep Learning, Machine Learning, Python
- An overview of synthetic data types and generation methods - Feb 22, 2021.
Synthetic data can be used to test new products and services, validate models, or test performances because it mimics the statistical property of production data. Today you'll find different types of structured and unstructured synthetic data.
Autoencoder, GANs, Generative Adversarial Network, Synthetic Data
- Unsupervised Learning for Predictive Maintenance using Auto-Encoders - Jan 14, 2021.
This article outlines a machine learning approach to detect and diagnose anomalies in the context of machine maintenance, along with a number of introductory concepts, including: Introduction to machine maintenance; What is predictive maintenance?; Approaches for machine diagnosis; Machine diagnosis using machine learning
Autoencoder, Predictive Analytics, Predictive Maintenance, Unsupervised Learning
- Recreating Fingerprints using Convolutional Autoencoders - Mar 4, 2020.
The article gets you started working with fingerprints using Deep Learning.
Autoencoder, Convolutional Neural Networks, Neural Networks, Python
Top 10 AI, Machine Learning Research Articles to know - Jan 30, 2020.
We’ve seen many predictions for what new advances are expected in the field of AI and machine learning. Here, we review a “data set” based on what researchers were apparently studying at the turn of the decade to take a fresh glimpse into what might come to pass in 2020.
2020 Predictions, Adversarial, Anomaly Detection, Autoencoder, Convolutional Neural Networks, Graph Theory, NLP, Transformer, Trends
- Neural Networks 201: All About Autoencoders - Nov 21, 2019.
Autoencoders can be a very powerful tool for leveraging unlabeled data to solve a variety of problems, such as learning a "feature extractor" that helps build powerful classifiers, finding anomalies, or doing a Missing Value Imputation.
Autoencoder, Machine Learning, Missing Values, Neural Networks
- Interpolation in Autoencoders via an Adversarial Regularizer - Mar 29, 2019.
Adversarially Constrained Autoencoder Interpolation (ACAI; Berthelot et al., 2018) is a regularization procedure that uses an adversarial strategy to create high-quality interpolations of the learned representations in autoencoders.
Adversarial, AISC, Autoencoder, Machine Learning
- Variational Autoencoders Explained in Detail - Nov 30, 2018.
We explain how to implement VAE - including simple to understand tensorflow code using MNIST and a cool trick of how you can generate an image of a digit conditioned on the digit.
Autoencoder, Deep Learning, Machine Learning, MNIST, TensorFlow
- How GOAT Taught a Machine to Love Sneakers - Aug 7, 2018.
Embeddings are a fantastic tool to create reusable value with inherent properties similar to how humans interpret objects. GOAT uses deep learning to generate these for their entire sneaker catalogue.
Autoencoder, Deep Learning, Image Recognition, Word Embeddings
- Deep Learning, The Curse of Dimensionality, and Autoencoders - Mar 12, 2015.
Autoencoders are an extremely exciting new approach to unsupervised learning and for many machine learning tasks they have already surpassed the decades of progress made by researchers handpicking features.
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Autoencoder, Deep Learning, Face Recognition, Geoff Hinton, Image Recognition, Nikhil Buduma
