Date: November 29, 2017 Location: University of Applied Social Sciences. Kalvarijų str. 137E, LT-08221 Vilnius, Lithuania Abstract Agenda Course objectives Target audience Technical Prerequisites About the trainer
Leonardo De Marchi
About the workshop
As the computer systems are getting more complicated, testers and people responsible for testing have to manage testing efforts that are getting bigger and bigger and changing often. Traditional testing on its own is not enough anymore to keep up with the development. Exploratory testing makes it possible to utilize testing experience and to carry out all the activities more efficient, and thus enables spending the time saved in documentation in a more sensible way. During this course you learn about exploratory testing in an interactive way with practical exercises. Interactive exercises improve the challenging thinking skills of exploratory testing. You also learn about the matters affecting the choices of how to conduct testing and about managing exploratory testing.
Hands-on: Setting up environment and tools 45min
Theory: Terminology and related techniques. Recap of basic topics related to the course
Basic concepts like perceptron, neural layers CNN 45 min
Hands-on: Code session to gain practical experience on perceptron and CNN 2h
Theory: Different types of networks and their use cases 45 min
Hands-on: Exploring more advanced topics: 45 min
Theory: Other neural network architecture 45min
In this training, we will see some Deep Learning concepts with Keras. The goal is to provide all the tools and knowledge to make the audience able to start their own Deep Learning projects. We will examine a real image recognition project and use it to show the process to develop the model from start to end.
We will start examining the business needs and designing the solution. We will explain how to create a multi layer network and then we will go through more sophisticated topics such as implementing different types of networks (for example Convolutional Neural Network) for Image Recognition, using dropouts and random noise to improve results, select the proper architecture and use pre-trained models.
We will also address some of the challenge of implementing it in production.
Workshop is intended for Data Scientists, Analysts and Developers that plans to start their first deep learning project, to save them time and resources and provide all the basic informations to get started straight away.
Attendants should have a laptop and Python installed with pip available. They should also download this images https://drive.google.com/open?id=0BwHyuAk55elgNllqYlNaWkZGTzg that will be used as a data set for the workshop.
Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sport world, with clients such as New York Knicks and Manchester United, and with large social networks, like Justgiving. He now works as Lead Data Scientist in Badoo, the largest dating site with over 360 million users.
Twitter (twitter.com/demarchileo) and LinkedIn (https://www.linkedin.com/in/leonardo-de-marchi).
Date: November 29, 2017
Location: University of Applied Social Sciences. Kalvarijų str. 137E, LT-08221 Vilnius, Lithuania
About the trainer