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Image recognition, speech recognition, and natural language processing are among the challenging problems for which neural networks and deep learning can provide solutions. A neural network can learn the weights and biases of its artificial neurons from training examples using stochastic gradient descent. A backpropagation algorithm makes it practical to use neural networks to solve problems previously thought to be insoluble. Iterating a deep neural network over too many training epochs can result in overfitting, but there are ways to ameliorate that problem. This talk will introduce the sigmoid artifical neuron, and the core ideas and principles behind a feedforward neural network, deep learning, stochastic gradient descent, the four backpropagation equations and the backpropagation algorithm. It will also introduce additional approaches, besides increasing size of training data, to reduce overfitting, such as L2 regularization, dropout, and artificially expanding the training data.

This talk will provide an introduction that will help attendees better understand more specialized topics that will be presented as part of our ML series in the fall.

CL Kim works in Software Engineering at CarGurus, Inc. He earned a Bachelor of Engineering from the National University of Singapore. He has an MBA and a Master of Science in Computer and Information Science from the University of Pennsylvania. He taught the well-rated IEEE Boston Section class on introduction to the Android platform and API.

This joint meeting of the Boston Chapter of the IEEE Computer Society and GBC/ACM will be online only due to the COVID-19 lockdown.