UCA Deep Learning School

TheNVIDIA Deep Learning Institutedelivers hands-on training for developers, data scientists, and engineers. The program isdesigned tohelp you get started with training, optimizing, and deploying neural networks to solve real-world problems across diverse industries such as self-driving cars, healthcare, online services,androbotics.

NVIDIA DEEP LEARNING INSTITUTE

L'Université Côte d'Azur et le <A HREF="http://www.nvidia.co.uk/dli" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">NVIDIA Deep Learning Institute</A> (DLI) propose une introduction poussée au Deep Learning au cours de cette université de printemps du 12 au 15 juin 2016.
L'université proposera une alternance de cours introductifs et tutoriels en matiné et d'exercices pratiques l'arpès-midi.
Un accès à des instance GPU Cloud vous permettra de réaliser vous même ces labs Deep Learning.
L'inscription est obligatoire.
<HR>
 
DLI Workshop Attendee Instructions
Create a Qwiklabs account by going to <A HREF="https://nvlabs.qwiklab.com/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">https://nvlabs.qwiklab.com/</A> prior to getting to the workshop.
Ensure Qwiklabs runs smoothly on your laptop by going to <A HREF="http://websocketstest.com/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">http://websocketstest.com/</A><A HREF="http://websocketstest.com/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer"></A><A HREF="http://websocketstest.com/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer"></A>
Make sure that WebSockets work for you by seeing under Environment, WebSockets is supported and Data Receive, Send and Echo Test all check Yes under WebSockets (Port 80).
If there are issues with WebSockets, try updating your browser. Best browsers for Qwiklabs are Chrome, FireFox and Safari. The labs will run in IE but it is not an optimal experience. 
You must bring your own laptop to these workshops.
<HR>
Agenda
Vous pouvez choisir d'assister à tout ou partie de cette école de printemps. Merci de vous enregistrer en fonction.
<HR>
Lundi 12 juin - matin - Introduction
Panorama du Deep Learning aujourd'hui : Stéphane Canu (Deep_In_France, INSA-Rouen)
Bases du Deep Learning (Perceptron, MLP, backpropagation, etc) : Frédéric Precioso (Deep_In_France, UCA)
Introduction aux Convolutional Neural Network (CNN) : Frédéric Precioso (Deep_In_France, UCA)
Lundi 12 juin - après-midi - lab Nvidia - Image classification & Object detection
Image Classification with NVIDIA DIGITS
Level: Beginner | Prerequisites: None Industry: All
This lab shows you how to leverage deep neural networks (DNN) - specifically convolutional neural networks (CNN) - within the deep learning workflow to solve a real-world image classification problem using NVIDIA DIGITS on top of the Caffe framework and the MNIST hand-written digits datasetin this lab, you will learn how to:
<UL>
<LI>Architect a Deep Neural Network to run on a GPU</LI>
<LI>Manage the process of data preparation, model definition, model training and troubleshooting</LI>
<LI>Use validation data to test and try different strategies for improving model performance</LI>
</UL>
 On completion of this lab, you will be able to use <A HREF="https://developer.nvidia.com/digits" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">NVIDIA DIGITS</A> to architect, train, evaluate and enhance the accuracy of CNNs on your own image classification application. 
Object Detection with NVIDIA DIGITS
Level: Beginner-Intermediate | Prerequisites: Image Classification with NVIDIA DIGITS Industry: All
This lab introduces students to one of four primary computer vision tasks - object detection - by trying three different approaches: sliding window, fully convolutional network (FCN), and <A HREF="https://developer.nvidia.com/digits" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">DIGITS</A>’ DetectNet network modelin this lab, you will learn how to:
<UL>
<LI>Measure object detection approaches in relation to three metrics: model training time, model accuracy and speed of detection during deployment</LI>
<LI>Implement a sliding window approach to object detection</LI>
<LI>Convert fully connected networks to fully convolutional networks (FCN)</LI>
<LI>Use DIGITS’ DetectNet for more efficient object detection</LI>
</UL>
On completion of this lab, you will understand the merits of each approach and learn how to detect objects using neural networks trained using NVIDIA DIGITS on the Caffe framework on real-world datasets.
Mardi 13 juin - matin - Autoencodeurs, GAN & Transfer learning
Cours autoencodeurs et Restricted Boltzman Machines : Soufiane Belharbi (Deep_In_France, INSA-Rouen)
Generative Adversarial Networks : Mélanie Ducoffe (Deep_In_France, UCA)
Transfer learning with CNN : Soufiane Belharbi (Deep_In_France, INSA-Rouen)
Mardi 13 juin - après-midi - lab NVIDIA - Medical Image Segmentation & Photo Editing with GAN
Medical Image Segmentation with NVIDIA DIGITS
Level: Beginner | Prerequisites: None Industry: Healthcare
This lab explores various approaches to the problem of semantic image segmentation, which is a generalization of image classification where class predictions are made at the pixel level. We use the Sunnybrook Cardiac Data to train a neural network to learn to locate the left ventricle on MRI images. In this lab,you will learn how to:
<UL>
<LI>Use popular image classification neural networks for semantic segmentation</LI>
<LI>Extend Caffe with custom Python layers</LI>
<LI>Become familiar with the concept of transfer learning</LI>
<LI>Train two Fully Convolutional Networks (FCNs).</LI>
</UL>
On completion of this lab, you will be able to set up your own image segmentation workflow in DIGITS and adapt it to a medical use case. 
Photo Editing with Generative Adversarial Networks with Tensorflow and DIGITS
Learn how to train a GAN to generate images of handwritten digits in DIGITS using the MNIST dataset, then apply them on the CelebA dataset of celebrity faces.
Mercredi 14 Juin - matin - RNN, Visual Question answering & image captioning
Cours sur les Reccurrent Neural Network: Rémi Cadène (Deep_In_France, LIP6)
Visual Question Answering: Rémi Cadène (Deep_In_France, LIP6)
Attention model pour image captioning: Jakob Verbeek (Deep_In_France, INRIA Rhone-Alpes)
Mercredi 14 Juin - après-midi - lab NVIDIA - Time Series avec RNN (données médicales)
Modelling Time Series Data with Recurrent Neural Networks in Keras
Level: Intermediate | Prerequisites: Some experience training CNNs Industry: Healthcare
One important area of current research is the use of deep neural networks to classify or forecast time-series data. Time-series data is produced in large volumes from sensors in a variety of application domains including Internet of Things (IoT), cyber security, data center management and medical patient care. In this lab,you will learn how to:
<UL>
<LI>Create training and testing datasets using electronic health records in HDF5 (hierarchical data format version five)</LI>
<LI>Design a high-performance model</LI>
<LI>Prepare datasets for use with recurrent neural networks (RNNs), which allows modeling of very complex data sequences</LI>
</UL>
On completion of this lab, you will be able to construct a long-short term memory model (LSTM), a specific RNN architecture, using the Keras library running on top of Theano to evaluate model performance against baseline data.
Jeudi 15 juin - matin - optimisation, Neural fabrics & active learning
Optimisation Deep Networks: Mélanie Ducoffe/Soufiane Belharbi/Frédéric Precioso (Deep_In_France)
Convolutional neural fabrics: Jakob Verbeek (Deep_In_France, INRIA Rhone-Alpes)
Active learning for Deep Nets: Mélanie Ducoffe (Deep_In_France, UCA)
Jeudi 15 juin - après-midi - Lab Nvidia - Analyse du signal en Deep Learning, Mise en production d'algorithme Deep Learning  
Signal Processing Using NVIDIA DIGITS
Level: Beginner | Prerequisites: None Industry: All
This lab demonstrates how to process data from acoustic, seismic, radio, and radar sensorsin this lab, you will learn how to:
<UL>
<LI>Utilize a Convolutional Neural Network (CNN) to process Radio Frequency (RF) signals</LI>
<LI>Detect a weak signal corrupted by noise</LI>
</UL>
On completion of this lab, you will be able to leverage the DIGITS application to read in a dataset, train a CNN, adjust hyper-parameters and then test and evaluate the performance of your model.
Neural Network Deployment with NVIDIA DIGITS and TensorRT
Level: Beginner | Prerequisites: Image Classification Using NVIDIA DIGITS Industry: All
In this lab,you will learn how to:
<UL>
<LI>Understand the role of batch size in inference performance</LI>
<LI>Make various optimizations in the inference process.</LI>
<LI>Explore inference for a variety of different DNN architectures trained in other DLI labs. </LI>
</UL>
On completion of this lab, you will be able to execute a full Deep Learning workflow: from loading data, to training a neural network, to deploying that trained network to production.
<HR>
DLI Workshop Attendee Instructions
Create a Qwiklabs account by going to <A HREF="https://nvlabs.qwiklab.com/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">https://nvlabs.qwiklab.com/</A> prior to getting to the workshop.
Ensure Qwiklabs runs smoothly on your laptop by going to <A HREF="http://websocketstest.com/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">http://websocketstest.com/</A><A HREF="http://websocketstest.com/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer"></A><A HREF="http://websocketstest.com/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer"></A>
Make sure that WebSockets work for you by seeing under Environment, WebSockets is supported and Data Receive, Send and Echo Test all check Yes under WebSockets (Port 80).
If there are issues with WebSockets, try updating your browser. Best browsers for Qwiklabs are Chrome, FireFox and Safari. The labs will run in IE but it is not an optimal experience. 
You must bring your own laptop to these workshops.

UCA Deep Learning School

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