Tag Archives: Hidden Units

LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning [Rerun]

LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning [Rerun] Episode Summary: In this episode we describe how to download and use free nonlinear machine learning software for implementing a Perceptron learning machine with a single layer of Radial Basis Function hidden units for the purposes of supervised learning. Show Notes: Welcome to the 51st podcast… Read More »

LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)

LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging) Episode Summary: Deep learning machine technology has rapidly developed over the past five years due in part to a variety of factors such as: better technology, convolutional net algorithms, rectified linear units, and a relatively new learning strategy called “dropout” in which hidden… Read More »

LM101-023: How to Build a Deep Learning Machine (Function Approximation)

Episode Summary: In this episode we discuss how to design and build “Deep Learning Machines” which can autonomously discover useful ways to represent knowledge of the world. Show Notes: Hello everyone! Welcome to the twenty-third podcast in the podcast series Learning Machines 101. In this series of podcasts my goal is to discuss important concepts of artificial intelligence… Read More »