Presented By O'Reilly and Cloudera
Make Data Work
31 May–1 June 2016: Training
1 June–3 June 2016: Conference
London, UK

Deep learning conference sessions

11:30–12:00 Wednesday, 1/06/2016
Alexandre Dalyac (Tractable), Robert Hogan (Tractable)
The bottleneck in computer vision is in creating sufficiently large, labeled training sets for tasks. Alexandre Dalyac and Robert Hogan address this issue through a combination of dimensionality reduction, information retrieval, and domain adaptation techniques packaged in a software product that acts as a human-algorithm interface to facilitate transfer of expertise from human to machine.
14:05–14:45 Friday, 3/06/2016
Alyona Medelyan (Thematic)
With the rise of deep learning, natural language understanding techniques are becoming more effective and are not as reliant on costly annotated data. This leads to an explosion of possibilities of what businesses can do with language. Alyona Medelyan explains what the newest NLU tools can achieve today and presents their common use cases.
11:15–11:55 Friday, 3/06/2016
Anirudh Koul (Microsoft), Saqib Shaikh (Microsoft)
Anirudh Koul and Saqib Shaik explore cutting-edge advances at the intersection of vision, language, and deep learning that help the blind community "see" the physical world and explain how developers can utilize this state-of-the-art image-captioning and computer-vision technology in their own applications.
11:15–11:55 Thursday, 2/06/2016
Andy Petrella (Kensu), Melanie Warrick (Google)
Deep learning is taking data science by storm, due to the combination of stable distributed computing technologies, increasing amounts of data, and available computing resources. Andy Petrella and Melanie Warrick show how to implement a Spark­-ready version of the long short­-term memory (LSTM) neural network, widely used in the hardest natural language processing and understanding problems.
12:00–12:30 Wednesday, 1/06/2016
Piotr Mirowski (Google DeepMind)
Piotr Mirowski looks under the hood of recurrent neural networks and explains how they can be applied to speech recognition, machine translation, sentence completion, and image captioning.
16:35–17:15 Friday, 3/06/2016
Kanu Gulati (Zetta Venture Partners)
Hardware accelerated solutions are ready to meet challenges in data collection, exploration, and visualization. Simply stated, data analytics and high-performance computing evolution must go hand in hand. Kanu Gulati provides an overview of the advances in hardware acceleration and discusses specific real-world use cases of HPC applications that are enabling innovation in analytics.
14:00–14:30 Wednesday, 1/06/2016
Olivier Grisel (Inria & scikit-learn)
Deep learning leverages compositions of parametrized differentiable modules commonly referred to as neural networks to build versatile and powerful predictive models from richly annotated data. Olivier Grisel offers an overview of recent trends and advances in deep learning research in computer vision, natural language understanding, and agent control via reinforcement learning.
9:50–9:55 Thursday, 2/06/2016
Piotr Piotr (deepsense.io)
Piotr Niedźwiedź explores how deepsense.io created the world’s best deep learning model for identifying individual right whales using aerial photography for the NOAA (National Oceanic and Atmospheric Administration) and explains what happened when the solution was covered by news media around the globe.
12:05–12:45 Thursday, 2/06/2016
Sherry Moore (Google)
TensorFlow is an open source software library for numerical computation with a focus on machine learning. Its flexible architecture makes it great for research and production deployment. Sherry Moore offers a high-level introduction to TensorFlow and explains how to use it to train machine-learning models to make your next application smarter.
11:15–11:55 Thursday, 2/06/2016
Cliff Click (0xdata)
H2O is an in-memory, big-data, big-math machine-learning platform. Cliff Click offers a technical talk focused on the insides of H2O. Cliff explains how you can write simple, single-threaded Java code and have H2O autoparallelize and auto-scale-out to hundreds of nodes and thousands of cores.
11:15–11:55 Thursday, 2/06/2016
Robert Bogucki (deepsense.io), Maciej Klimek (deepsense.io)
With fewer than 500 North Atlantic right whales left in the world's oceans, knowing the health and status of each whale is integral to the efforts of researchers working to protect the species from extinction. To interest the data science community, NOAA Fisheries organized a competition hosted on Kaggle.com. Robert Bogucki and Maciej Klimek outline the winning solution.