Presented by :  Ashwinkumar Ganesan, PhD student

1 pm – 2 pm Friday, November 17 2017 , ITE 325 UMBC

In recent years, Deep Neural Networks have been highly successful at performing a number of tasks in computer vision, natural language processing and artificial intelligence in general. The remarkable performance gains have led to universities and industries investing heavily in this space. This investment creates a thriving open source ecosystem of tools & libraries that aid the design of new architectures, algorithm research as well as data collection.


This talk (and hands-on session) introduce people to some of the basics of machine learning, neural networks and discusses some of the popular neural network architectures. We take a dive into one of the popular libraries, Tensorflow, and an associated abstraction library Keras.

Workshop requirements: Laptop

Following are the list of libraries to be installed:
1. numpy, scipy & scikit-learn.
2. tensorflow / tensoflow-gpu. (The first one is the GPU version).
3. matplotlib for visualizations (if necessary).
4. jupyter & ipython. (We will use python2.7 in our experiments).
Following are helpful links:
All of the above can be installed using pip. In case of windows or (any other OS) consider doing an installation of anaconda that has all the necessary libraries.