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The Deep Learning Video Collection: 2016

Video Description

Learn about the very latest in deep learning techniques for tools such as TensorFlow, Spark, and GraphLab with this video collection of every talk on deep learning from these 2016 conferences: the five Strata + Hadoop World conferences plus O'Reilly's inaugural Artificial Intelligence Conference. You'll see world-class experts on deep learning explain how they implement deep neural networks, address common challenges, manage distributed training at scale, and more.

With The Deep Learning Video Collection: 2016, you get the full year of deep learning talks. Here's how it works: During the year, after each conference wraps up, we'll add a fresh batch of videos to Safari. This year's conferences are being held in June, August, September and December.

Content from Strata + Hadoop World San Jose, held in March 2016, is already available, and includes 10 talks from experts in deep learning.

Table of Contents

  1. San Jose 2016: Deep Learning Tools
    1. Creating intelligence: An applications-first approach to machine learning - Carlos Guestrin (Dato Inc.)
    2. Deploying deep learning at scale - Naveen Rao (Nervana)
    3. TensorFlow: Machine learning for everyone - Rajat Monga (Google)
    4. TensorFlow: Large-scale analytics and distributed machine learning with TensorFlow, BigQuery, and Dataflow (Apache Beam) - Kazunori Sato (Google), Amy Unruh (Google)
    5. SparkNet: Training deep networks in Spark - Robert Nishihara (UC Berkeley)
  2. San Jose 2016: Deep Learning Techniques
    1. Afraid of the future? You should be. Deep learning is eating your lunch—and mine. - Arno Candel (
    2. Large-scale product classification via text and image-based signals using a fusion of discriminative and deep learning-based classifiers - Sreeni Iyer (quadanalytix), Anurag Bhardwaj (Quad Analytix)
    3. A scalable implementation of deep learning on Spark - Alexander Ulanov (Hewlett-Packard Labs)
  3. San Jose 2016: Deep Learning Applications
    1. Deep learning and recurrent neural networks applied to electronic health records - Josh Patterson (Patterson Consulting), David Kale (University of Southern California), Zachary Lipton (University of California, San Diego)
    2. Can deep neural networks save your neural network? Artificial intelligence, sensors, and strokes - Brandon Ballinger (Cardiogram), Johnson Hsieh (Cardiogram)
  4. London 2016: Deep Learning Tools
    1. Recent advances in deep learning research - Olivier Grisel (Inria & scikit-learn)
    2. Which whale is it anyway? Face recognition for right whales using deep learning - Robert Bogucki ( and Maciej Klimek (
    3. Deep learning and natural language processing with Spark - Andy Petrella (Data Fellas) and Melanie Warrick (Skymind)
    4. The innards of H2O - Cliff Click (0xdata)
  5. London 2016: Deep Learning Techniques
    1. Opportunities for hardware acceleration in big data analytics - Kanu Gulati (Zetta Venture Partners)
    2. Applications of natural language understanding: Tools and technologies - Alyona Medelyan (Entopix)
  6. London 2016: Deep Learning Applications
    1. Deep learning for web-scale text - Piotr Mirowski (Google DeepMind)
    2. Visual data analysis for intelligent machines - Francesca Odone (University of Genova)
    3. Beyond guide dogs: How advances in deep learning can empower the blind community - Anirudh Koul (Microsoft) and Saqib Shaikh (Microsoft)