DEEP LEARNiNG WITH MULTIPLE DATA TYPES

HYBRID NEURAL NETWORK ARCHITECTURES

Instructors

Luigi Troiano
University of Salerno

Francesco Gissi
University of Sannio

Vincenzo Benedetto
University of Sannio

Full-day Workshop

2020 EVENTS

This workshop explores how convolutional and recurrent neural networks can be combined to generate effective descriptions of content within images and video clips.

Learn how to train a network using TensorFlow and the Microsoft Common Objects in Context (COCO) dataset to generate captions from images and video by:

  • Implementing deep learning workflows like image segmentation and text generation

  • Comparing and contrasting data types, workflows, and frameworks

  • Combining computer vision and natural language processing

Upon completion, you’ll be able to solve deep learning problems that require multiple types of data inputs.

Prerequisites: Familiarity with basic Python (functions and variables); prior experience training neural networks.

Technologies: TensorFlow

NOV

11

09h30
(CEST)

ONLINE

JAN

10

10h00

University of Sannio

Dipartimento di Ingegneria (DING)

Palazzo Bosco Lucarelli

Primo Piano - Laboratorio Polifunzionale

82100 Benevento (Italy)