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Deep Learning Italia – Milano – 19 ottobre 2018

Il Meetup #AperiTech di ottobre di Deep Learning Italia!

• Cosa faremo
“Deep Learning Italia” è lieta di invitarvi al suo Meetup a Milano.

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Scaletta Talks:

1) Matteo Testi Founder Deep Learning Italia & Luciano Di Benedetto Head of Big Data at Sferanet

“Introduzione alla piattaforma Deep Learning Italia”
Abstract: Illustrazione della community Deep Learning Italia e degli strumenti oggi sviluppati e a disposizione della community. Introduzione alle features del sito i tutorials, il question & answer, le references, gli sviluppi futuri.

2) SK Reddy Chief Product Officer AI for Hexagon, located in Silicon Valley

Title: Making sense with 3D Point Clouds using Deep Learning

Abstract: Processing 3D images has many use cases. For example, to improve autonomous car driving, to enable digital conversions of old factories, enable augmented reality solutions for medical surgeries, etc. 3D image processing brings enormous benefits but also amplifies computing cost. The size of the point cloud, the number of points, sparse and irregular point cloud, and the adverse impact of the light reflections, (partial) occlusions, etc., make it difficult for engineers to process point clouds. Moving from using handcrafted features to using deep learning techniques to semantically segment the images, to classify objects, to detect objects, to detect actions in 3D videos, etc., we have come a long way in 3D image processing. 3D Point Cloud image processing is increasingly used to solve Industry 4.0 use cases to help architects, builders and product managers. I will share some of the innovations that are helping the progress of 3D point cloud processing. I will share the practical implementation issues we faced while developing deep learning models to make sense of 3D Point Clouds.



3) Matteo Alberti Techedge & Deep Learning Italia

Applicazione dei principali algoritmi di Deep Learning al mondo Finance:

0) I limiti della modellistica AR, ARIMA,ARMA

1) Applicazione di Reti Ricorrenti (LSTM)

2) Dalle Convoluzioni 2D all’estrazione di Pattern

3) Strumenti di Riduzione della dimensionalità per inglobare indici nella nostra predizione (dalle linearità ad Autoencoder)

4) Cenni al Reinforcement Learning per Automated Stock Trading

Il tutto con alucni esempi pratici con codice in Tensorflow