The Deep Learning Video Collection: 2016

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 (H2O.ai)
   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 (deepsense.io) and Maciej Klimek (deepsense.io)
   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)
7. New York 2016: Deep Learning Tools
   1. Deep learning with TensorFlow - Martin Wicke (Google) and Josh Gordon (Google) - Part 1
   2. Deep learning with TensorFlow - Martin Wicke (Google) and Josh Gordon (Google) - Part 2
   3. Fast deep learning at your fingertips - Amitai Armon (Intel) and Nir Lotan (Intel)
   4. Machine intelligence at Google scale - Kazunori Sato (Google)
   5. Changing the landscape with deep learning and accelerated analytics - Jim McHugh (NVIDIA), Eric Kontargyris (MapD), Mike Perez (Kinetica), and Mike Wendt (Accenture)
8. New York 2016: Deep Learning Techniques
   1. Recent advances in applications of deep learning for text and speech - Yishay Carmiel (Spoken Communications)
   2. Removing complexity from scalable machine learning - Martin Wicke (Google)
   3. Semantic natural language understanding with Spark Streaming, UIMA, and machine-learned ontologies - David Talby (Atigeo) and Claudiu Branzan (G2 Web Services)
9. New York 2016: Deep Learning Applications
   1. Why should I trust you? Explaining the predictions of machine-learning models - Carlos Guestrin (University of Washington Apple)
   2. Conditional recurrent neural nets, generative AI Twitter bots, and DL4J - David Kale (University of Southern California) and Josh Patterson (Skymind)
10. Singapore 2016: Deep Learning Tools
    1. Deep learning with TensorFlow - Wolff Dobson (Google) and Josh Gordon (Google) - Part 1
    2. Deep learning with TensorFlow - Wolff Dobson (Google) and Josh Gordon (Google) - Part 2
    3. Deep reinforcement learning on Spark - Adam Gibson (Skymind)
    4. Fast deep learning at your fingertips - Nir Lotan (Intel)
    5. Web-scale machine learning on Apache Spark - Jason (Jinquan) Dai (Intel)
11. Singapore 2016: Deep Learning Techniques
    1. Building and tuning machine-learning apps using Spark ML and GraphX Libraries - Jayant Shekhar (Sparkflows Inc.) and Vartika Singh (Cloudera) - Part 1
    2. Building and tuning machine-learning apps using Spark ML and GraphX Libraries - Jayant Shekhar (Sparkflows Inc.) and Vartika Singh (Cloudera) - Part 2
    3. Building and tuning machine-learning apps using Spark ML and GraphX Libraries - Jayant Shekhar (Sparkflows Inc.) and Vartika Singh (Cloudera) - Part 3
    4. Building and tuning machine-learning apps using Spark ML and GraphX Libraries - Jayant Shekhar (Sparkflows Inc.) and Vartika Singh (Cloudera) - Part 4
    5. Deep learning at scale - Mateusz Dymczyk (H2O.ai)
    6. Deep learning for natural language processing - Bargava Subramanian (Cisco Systems) and Amit Kapoor (narrativeVIZ Consulting)
    7. Experience in adopting deep learning into existing software development practices - Verdi March (Deep Labs)
    8. Transfer learning and fine-tuning deep neural network models across different domains - Anusua Trivedi (Microsoft)
12. Singapore 2016: Deep Learning Applications
    1. Applications of natural language understanding: Tools and technologies - Alyona Medelyan (Thematic)
    2. High-efficiency systems for distributed AI and machine learning at scale - Qirong Ho (Petuum, Inc.)