Friday 9:00–10:30 in Tower Suite 2

Computer Vision: An (Un?)Expected Journey, with Keras and Tensorflow

Rodolfo Bonnin

Audience level:


A hands-on tutorial which will allow you to practice and finally train cutting-edge Deep Learning models, covering the most active and demanded skills in the field: Image Classification, Feature detection and Image Segmentation.


This tutorial will allow you to grasp of the fundamental concepts you need to solve common Computer Vision problems (Classification, Detection, and Segmentation), using state of the art Deep Neural Models, with the help of two of the most well known Machine Learning libraries, Keras and Tensorflow.

This talk is ideal for Python Programmers who want to have a quick and practical introduction to Computer Vision, and to be able to apply these concepts solving their own problems

Pre-requisites: The participants should have a basic/intermediate knowledge of the Python language, and the main Machine Learning concepts. The training itself requires bringing a notebook Jupyter Notebook along with the Keras library installed. Some of the tasks are computationally expensive, so a powerful notebook will reduce the training time greatly. Having the last version of Anaconda installed, with keras and opencv additional packages installed via "conda install keras opencv" will be definitely a plus.

Strategy: Jupyter notebooks and sample datasets will be provided to allow a quick setup and running of the initial tasks, and at the same time allowing for free experimentation and enrichening of the models, as the training progresses.


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