Presented By O'Reilly and Cloudera
Make Data Work
22–23 May 2017: Training
23–25 May 2017: Tutorials & Conference
London, UK

Schedule: Deep learning sessions

9:00 - 17:00 Monday, 22 May & Tuesday, 23 May
Data science and advanced analytics
Location: Capital Suite 17
Robert Schroll (The Data Incubator)
Robert Schroll demonstrates TensorFlow's capabilities through its Python interface, walking you through building machine-learning algorithms piece by piece and using the higher-level abstractions provided by TensorFlow. You'll then use this knowledge to build machine-learning models on real-world data. Read more.
9:0012:30 Tuesday, 23 May 2017
SOLD OUT
Data science and advanced analytics
Location: Capital Suite 13
Level: Intermediate
Alison Lowndes (NVIDIA)
Average rating: **...
(2.50, 4 ratings)
Alison Lowndes leads a hands-on exploration of approaches to the challenging problem of detecting if an object of interest is present within an image and, if so, recognizing its precise location within the image. Along the way, Alison walks you through testing three different approaches to deploying a trained DNN for inference. Read more.
9:0017:00 Tuesday, 23 May 2017
Location: London Suite 2/3
Angie Ma (Faculty), Ben Lorica (O'Reilly Media), Ira Cohen (Anodot), Yingsong Zhang (ASI Data Science), Ali Hürriyetoglu (Statistics Netherlands), Nelleke Oostdijk (Radboud University), Robin Senge (inovex GmbH), Mathew Salvaris (Microsoft), Miguel Gonzalez-Fierro (Microsoft), Amitai Armon (Intel), Yahav Shadmi (Intel), Kay Brodersen (Google), Ding Ding (Intel), Alan Mosca (nPlan | Birkbeck, University of London), Eduard Vazquez (Cortexica Vision Systems), Aida Mehonic (The Alan Turing Institute), David Barber (UCL)
A full day of hardcore data science, exploring emerging topics and new areas of study made possible by vast troves of raw data and cutting-edge architectures for analyzing and exploring information. Along the way, leading data science practitioners teach new techniques and technologies to add to your data science toolbox. Read more.
13:3017:00 Tuesday, 23 May 2017
Data science and advanced analytics
Location: Capital Suite 13
Level: Advanced
Anima Anandkumar (UC Irvine)
Average rating: ***..
(3.67, 3 ratings)
Deep learning is the state of the art in domains such as computer vision and natural language understanding. Apache MXNet is a highly flexible and developer-friendly deep learning framework. Anima Anandkumar provides hands-on experience on how to use Apache MXNet with preconfigured Deep Learning AMIs and CloudFormation Templates to help speed your development. Read more.
11:1511:55 Wednesday, 24 May 2017
Data science and advanced analytics
Location: Capital Suite 8/9
Level: Beginner
Yishay Carmiel (IntelligentWire)
Average rating: ***..
(3.00, 1 rating)
For years, people have been talking about the great promise of conversation AI. Recently, deep learning has taken us a few steps further toward achieving tangible goals, making a big impact on technologies like speech recognition and natural language processing. Yishay Carmiel offers an overview of the impact of deep learning, recent breakthroughs, and challenges for the future. Read more.
11:1511:55 Wednesday, 24 May 2017
Data science and advanced analytics
Location: Hall S21/23 (A)
Level: Intermediate
Mikio Braun (Zalando SE)
Average rating: ***..
(3.14, 7 ratings)
Deep learning has become the go-to solution for challenges such as image classification or speech processing, but does it work for all application areas? Mikio Braun offers background on deep learning and shares his practical experience working with these exciting technologies. Read more.
14:0514:45 Wednesday, 24 May 2017
Business case studies
Location: Hall S21/23 (A)
Level: Beginner
Kaz Sato (Google)
Average rating: ****.
(4.20, 5 ratings)
TensorFlow is democratizing the world of machine intelligence. With TensorFlow (and Google's Cloud Machine Learning platform), anyone can leverage deep learning technology cheaply and without much expertise. Kazunori Sato explores how a cucumber farmer, a car auction service, and a global insurance company adopted TensorFlow and Cloud ML to solve their real-world problems. Read more.
14:0514:45 Wednesday, 24 May 2017
Level: Intermediate
Average rating: ***..
(3.33, 3 ratings)
Deep learning is one of the most exciting techniques in machine learning. Miguel González-Fierro explores the problem of image classification using ResNet, the deep neural network that surpassed human-level accuracy for the first time, and demonstrates how to create an end-to-end process to operationalize deep learning in computer vision for business problems using Microsoft RServer and GPU VMs. Read more.
14:5515:35 Wednesday, 24 May 2017
Business case studies
Location: Capital Suite 7
Level: Intermediate
Josef Viehhauser (BMW Group), Dominik Schniertshauer (BMW Group)
Average rating: ****.
(4.60, 5 ratings)
Data-driven solutions based on machine and deep learning are gaining momentum in the automotive industry beyond autonomous driving. Josef Viehhauser and Dominik Schniertshauer explore use cases from the BMW Group where novel machine-learning pipelines (such as those based on XGBoost and convolutional neural nets, for example) support a broad variety of business stakeholders. Read more.
16:3517:15 Wednesday, 24 May 2017
Data science and advanced analytics
Location: Capital Suite 10/11
Level: Beginner
Jonathon Morgan (New Knowledge)
Average rating: *****
(5.00, 12 ratings)
Jonathon Morgan explores computer vision, deep learning, and natural language processing techniques for uncovering communities of white nationalists and neo-Nazis on social media and identifying which ones are on the path to radicalization. Read more.
16:3517:15 Wednesday, 24 May 2017
Data science and advanced analytics
Location: Capital Suite 7
Level: Beginner
Laura Froelich (Think Big Analytics, a Teradata Company)
Average rating: ***..
(3.00, 3 ratings)
Laura Frolich explores applications of deep learning in companies—looking at practical examples of assessing the opportunity for AI, phased adoption, and lessons going from research to prototype to scaled production deployment—and discusses the future of enterprise AI. Read more.
17:2518:05 Wednesday, 24 May 2017
Level: Intermediate
Chris Fregly (PipelineAI)
Average rating: ***..
(3.00, 1 rating)
Chris Fregly explores an often-overlooked area of machine learning and artificial intelligence—the real-time, end-user-facing "serving” layer in hybrid-cloud and on-premises deployment environments—and shares a production-ready environment to serve your notebook-based Spark ML and TensorFlow AI models with highly scalable and highly available robustness. Read more.
17:2518:05 Wednesday, 24 May 2017
Data science and advanced analytics
Location: Hall S21/23 (A)
Sherry Moore (Google)
Average rating: ***..
(3.60, 5 ratings)
Sherry Moore discusses TensorFlow progress and adoption over 2016 and looks ahead to TensorFlow efforts in future areas of importance, such as performance, usability, and ubiquity. Read more.
11:1511:55 Thursday, 25 May 2017
Data science and advanced analytics
Location: Hall S21/23 (A)
Level: Intermediate
Average rating: ****.
(4.00, 2 ratings)
Nikolay Manchev offers an overview of the restricted Boltzmann machine, a type of neural network with a wide range of applications, and shares his experience using it on Hadoop (MapReduce and Spark) to process unstructured and semistructured data at a scale. Read more.
11:1511:55 Thursday, 25 May 2017
Data science and advanced analytics, Sponsored
Location: Capital Suite 2/3
Radhika Rangarajan explains how Intel works with its users to build deep learning-powered big data analytics applications (object detection, image recognition, NLP, etc.) using BigDL. Read more.
12:0512:45 Thursday, 25 May 2017
Data science and advanced analytics
Location: Hall S21/23 (A)
Level: Intermediate
Barbara Fusinska (Google)
Average rating: ***..
(3.00, 5 ratings)
The popularity of deep learning is due in part to its capabilities in recognizing patterns from inputs such as images or sounds. Barbara Fusinska offers an overview of Microsoft Cognitive Toolbox, an open source framework offering various modules and algorithms enabling machines to learn like a human brain. Read more.
14:0514:45 Thursday, 25 May 2017
Data science and advanced analytics
Location: Hall S21/23 (A)
Level: Intermediate
Martin Görner (Google)
Average rating: ****.
(4.75, 12 ratings)
With TensorFlow, deep machine learning has transitioned from an area of research into mainstream software engineering. Martin Görner walks you through building and training a neural network that recognizes handwritten digits with >99% accuracy using Python and TensorFlow. Read more.
14:5515:35 Thursday, 25 May 2017
Data science and advanced analytics
Location: Hall S21/23 (A)
Level: Beginner
Nir Lotan (Intel), Barak Rozenwax (Intel)
Average rating: *****
(5.00, 3 ratings)
Barak Rozenwax and Nir Lotan explain how to easily train and deploy deep learning models for image and text analysis problems using Intel's Deep Learning SDK, which enables you to use deep learning frameworks that were optimized to run fast on regular CPUs, including Caffe and TensorFlow. Read more.
16:3517:15 Thursday, 25 May 2017
Level: Intermediate
Kamran Yousaf (Redis Labs)
Average rating: ***..
(3.50, 6 ratings)
Kamran Yousaf explains how to substantially accelerate and radically simplify common practices in machine learning, such as running a trained model in production, to meet real-time expectations, using Redis modules that natively store and execute common models generated by Spark ML and TensorFlow algorithms. Read more.
16:3517:15 Thursday, 25 May 2017
Level: Intermediate
Mads Ingwar (Think Big), Eliano Marques (Think Big)
Average rating: ****.
(4.50, 2 ratings)
Eliano Marques and Mads Ingwar share a case study on how to leverage data science to plan ship engine maintenance by warning about potential piston ring failure. Read more.