Dr. Brian Spiering is a Professor of Computer Science at the University of San Francisco and freelance consultant. He teaches humans the languages of computers (primarily Python) and teaches computers the languages of humans (through Natural Language Processing and Artificial Intelligence). He is active in the San Francisco tech community through volunteering and mentoring.
Keras is an amazing library that simplifies the coding of deep learning models. We'll begin with a brief intro to neural networks (NNs). Then demo the building of a convolutional neural net (CNN) layer-by-layer. By the end, you should be able to build your simple deep learning models and understand what each element of a neural network does.
Interest in deep learning and building artificial intelligence (AI) based applications has been growing in the past few years. The Keras library makes these techniques accessible by offering a high-level API capable of running on top of TensorFlow, CNTK, or Theano. Similar to Python in general, Keras puts user experience front and center by having an API designed for human beings (not machines). This makes Keras perfect for easy and fast prototyping. Keras also highly modular and easy to extend to more complex deep learning models. This talk will be an introduction to neural networks and the Keras API for building them. We'll walk step-by-step how to build a convolutional neural net (CNN).