Tag Archives: Convolutional Neural Networks

LM101-059: How to Properly Introduce a Neural Network

LM101-059: How to Properly Introduce a Neural Network Episode Summary: I discuss the concept of a “neural network” by providing some examples of recent successes in neural network machine learning algorithms and providing a historical perspective on the evolution of the neural network concept from its biological origins. Show Notes: Hello everyone! Welcome to the fifty-ninth podcast in… 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 »

LM101-029: How to Modernize Deep Learning with Rectilinear units, Convolutional Nets, and Max-Pooling

LM101-029: How to Modernize Deep Learning  with Rectilinear units,  Convolutional Nets, and Max-Pooling Episode Summary This podcast discusses the topics of rectilinear units, convolutional nets, and max-pooling relevant to deep learning which were inspired by my recent visit to the 3rd International Conference on Learning Representations (May 7-9, 2015) in San Diego. Specifically, commonly used techniques shared by… 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 »