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Introduction to deep learning with Watson Studio

June 4, 2019  |  Meetup

Recent advances in computer hardware and algorithms have enabled proliferation of the use of deep learning models in many practical applications. A number of open source frameworks for deep learning have become very popular. Those include Keras, TensorFlow, Caffe, and PyTorch. IBM Watson Studio provides several ways to build deep learning models using those frameworks, from Python Jupyter notebooks and RStudio to the graphical interface of the Neural Network Modeler. The latter allows graphical construction of deep learning models, with automatic Python code creation.
This workshop will first provide an introduction to the theory of traditional neural networks, then discuss convolutional and recurrent networks and their applications. Deep learning examples using the Keras library will be shown in Jupyter notebooks, RStudio, and the Neural network modeler. Attendees can get some hands-on experience with those tools. Model deployment strategies and the model interchange format ONNX will be discussed. Finally, we will examine the open source packages AIFairness360 and Adversarial Robustness Toolkit, developed at IBM in collaboration with IBM Research.
Participants should be familiar with fundamentals of statistical modeling, and will gain a basic understanding of some popular deep learning methods, including possible applications and available tools.
Please bring your laptop and sign up for a free IBM Cloud account in advance.

71 S. Wacker Dr., 6th floor, Chicago, Illinois, 60606, United States

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