Fast Deep Learning Prototypes with Tensorflow and Keras
https://rit.zoom.us/j/91216440248
Deep Neural Networks (DNN) are a powerful tool for computer vision, signal processing, and natural language processing tasks. The last few years have seen the development of a plethora of software tools for the development of DNNs. Many of these tools provide a library of building blocks that the engineer or researcher can assemble in whatever form they desire. However, there are some common use cases that are implemented nearly identically every time. This leads to a lot of boilerplate code that slows down development. In this tutorial we discuss using Tensorflow 2.0 with the Keras API to enable rapid prototyping of DNNs with a minimum of code. Within the hour we will have several functioning Convolutional Networks, an efficient data pipeline, training and prediction, and logging. Some basic knowledge of Deep Learning and Python is assumed, as this tutorial focuses on the tool.
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- Rochester Institute of Technology
- Rochester, New York
- United States 14623
- Starts 18 April 2020 02:00 PM UTC
- Ends 30 April 2020 03:59 PM UTC
- No Admission Charge
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Miguel
Fast Deep Learning Prototypes with Tensorflow and Keras
Deep Neural Networks (DNN) are a powerful tool for computer vision, signal processing, and natural language processing tasks. The last few years have seen the development of a plethora of software tools for the development of DNNs. Many of these tools provide a library of building blocks that the engineer or researcher can assemble in whatever form they desire. However, there are some common use cases that are implemented nearly identically every time. This leads to a lot of boilerplate code that slows down development. In this tutorial we discuss using Tensorflow 2.0 with the Keras API to enable rapid prototyping of DNNs with a minimum of code. Within the hour we will have several functioning Convolutional Networks, an efficient data pipeline, training and prediction, and logging. Some basic knowledge of Deep Learning and Python is assumed, as this tutorial focuses on the tool.
Biography:
Miguel Dominguez is a PhD candidate in Engineering at Rochester Institute of Technology set to graduate in Summer 2020. His research interests include graph and point cloud neural networks as well as speech processing.
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https://rit.zoom.us/j/91216440248
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Meeting ID: 912 1644 0248