AJM Note: ALTHOUGH NEARLY COMPLETE, references and discussion still being added to this blogpost. This note will be removed once the blogpost is completed – anticipated over the Memorial Day weekend, 2023. Note updated 12:30 AM, Hawai’i Time, Tuesday, May 29, 2023. This blogpost accompanies a YouTube vid on the same topic of the Evolution… Continue reading Evolution of NLP Algorithms through Latent Variables: Future of AI (Part 3 of 3)
Category: NN – Backpropagating Multilayer Perceptron (MLP)
Multilayer Perceptrons (MLPs) are feedforward neural networks typically trained using a stochastic gradient descent method such as backpropagation.
New Neural Network Class: Framework: The Future of AI (Part 2 of 3)
We want to identify how and where the next “big breakthrough” will occur in AI. We use three tools or approaches to identify where this next big breakthrough will occur: The Quick Overview Get the quick overview with this YouTube #short: The Full YouTube Maren, Alianna J. 2023. “A New Neural Network Class: Creating the… Continue reading New Neural Network Class: Framework: The Future of AI (Part 2 of 3)
Kuhnian Normal and Breakthrough Moments: The Future of AI (Part 1 of 3)
Over the past fifty years, there have only been a few Kuhnian “paradigm shift” moments in neural networks. We’re ready for something new!
When a Classifier Acts as an Autoencoder, and an Autoencoder Acts as a Classifier (Part 1 of 3)
One of the biggest mental sinkholes into which AI students can get trapped is not quite understanding the fundamental difference between how our two basic “building block” networks operate: the Multilayer Perceptron (MLP), trained with backpropagation (or any form of gradient descent learning), and the (restricted) Boltzmann machine (RBM), trained with contrastive divergence. It’s easy… Continue reading When a Classifier Acts as an Autoencoder, and an Autoencoder Acts as a Classifier (Part 1 of 3)
How Backpropagation and (Restricted) Boltzmann Machine Learning Combine in Deep Architectures
One of the most important things for us to understand, as we come into the “deep learning” aspect of AI (for the first time), is the relationship between backpropagation and the (restricted) Boltzmann machines, which we know comprise the essential core of various “deep learning” architectures. The essential idea in deep architectures is this: Each… Continue reading How Backpropagation and (Restricted) Boltzmann Machine Learning Combine in Deep Architectures