08:15 – 09:15 Registration with light breakfast
09:15 – 09:30 Welcome & Introduction
09:30 – 10:10 Accelerating AI from the cloud to the Edge
This session will cover Intel’s vision for Artificial Intelligence and introduce the latest Intel portfolio of Hardware, Software and Services from a software development and AI perspective. Besides the architectural details of the latest Intel® Xeon® Scalable processor family, we will also cover the whole spectrum of hardware solutions up to the recently announced Intel® Nervana™ Neural Network Processor (NNP).
Ralph de Wargny
10:10 – 10:40 AI concepts and use cases
In this session, we will explore the concepts and applications of Deep Learning, with a focus on real world applications using the Intel CPUs for training and inference.
10:40 – 11:15 Intel Nervana Software Stack – Overview & Implementation
This session will cover Intel Nervana’s software stack for AI, Machine Learning and Deep Learning: from low-level libraries like
MKL / MKL-DNN, CPU-optimized frameworks (incl. neon, Caffe, TensorFlow, Theano), development tools like VTune, the Intel
Python distribution, to the new Intel® Nervana™ Graph library (ngraph).
11:15 – 11:45 Coffee Break
11:45 – 12:45 Practical Frameworks Session 1: Using Optimized Caffe Framework
In this session we show how to build Caffe optimized for Intel architecture, train deep network models using one or more
compute nodes, and deploy networks. In addition, various functionalities of Caffe are explored in detail including how to finetune,
extract and view features of different models, and use the Caffe Python API.
12:45 – 13:30 Optimizing Python Code using the Intel Distribution of Python
It used to be the case that you would never use the words ‘performance’ and ‘python in the same sentence. The Intel distribution
of Python changes all that. In this second of a two-parts’ session we show how you can speed up you Python codes ‘out-of-thebox’
by using the Intel distribution of python. In this session we use the Intel optimized version of SciKit-Learn.
13:30 – 14:00 Case Study Manufacturing package fault detection using Deep Learning
A proof of concept focused on adopting deep-learning technology based on Caffe* for manufacturing package fault detection.
14:00 – 15:00 Lunch Break
15:00 – 15:30 Meet the engineers
15:30 – 16:15 Practical frameworks session 2: Using Tensorflow
In this tutorial we show how to use the Intel-optimized version of TensorFlow hosted on the high-level neural networks library
Keras. As well as demonstrating of how to use these frameworks, the session will include a ‘live’ VTune analysis of the
frameworks and an explanation of how the Intel implemented optimizations were achieved.
16:15 – 16:30 Coffee break
16:30 – 17:15 DL Inference using the movidius “neural” compute stick
Learn how trained models can be optimized for inference using innovative Movidius™ Neural Compute Stick.
17:15 – 17:30 Q&A and closing comments
17:30 – 18:30 Optional: guided tour of the Wanda Metropolitano, Atlético Madrid Stadium