Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

Deep Learning Summit (DLS01-1)

632 visualizaciones

Publicado el

Deep learning is having a profound impact on AI applications. With the future of neural network-inspired computing in mind, re:Invent is hosting the first ever Deep Learning Summit. Designed for developers to learn about the latest in deep learning research and emerging trends, attendees will hear from industry thought leaders—members of the academic and venture capital communities—who will share their perspectives in 30-minute Lightning Talks.

The Summit will be held on Thursday, November 30th at the Venetian from 1-5pm.

The Deep Learning Revolution - Terrence Sejnowski, The Salk Institute for Biological Studies
Eye, Robot: Computer Vision and Autonomous Robotics - Aaron Ames & Pietro Perona, California Institute of Technology
Exploiting the Power of Language - Alexander Smola, Amazon Web Services
Reducing Supervision: Making More with Less - Martial Herbert, Carnegie Mellon University
Learning Where to Look in Video - Kristen Grauman, University of Texas
Look, Listen, Learn: The Intersection of Vision and Sound - Antonio Torralba, MIT
Investing in the Deep Learning Future - Matt Ocko, Data Collective Venture Capital

Publicado en: Tecnología
  • Sé el primero en comentar

Deep Learning Summit (DLS01-1)

  1. 1. The Deep Learning Revolution Terrence Sejnowski, The Salk Institute, UCSD, HHMI
  2. 2. Stanley the Self-driving Car Sebastian Thrun
  3. 3. Marvin Minsky
  4. 4. Why Vision is a Hard Problem
  5. 5. Neurons As Processors
  6. 6. Neurons As Processors Synapses are plastic
  7. 7. Frank Rosenblatt
  8. 8. Perceptron
  9. 9. Perceptron
  10. 10. Limitations of Perceptrons Minsky and Papert, Perceptrons, 1969
  11. 11. 1854
  12. 12. De Novo NETtalk Sejnowski and Rosenberg, 1986
  13. 13. Carterette and Jones
  14. 14. Saddle Points in High Dimensions Error Weights
  15. 15. Visual Cortex Hubel and Wiesel, 1969
  16. 16. Hubel and Wiesel, 1962 Simple Cell
  17. 17. Complex Cell
  18. 18. Deep Learning
  19. 19. Geoffrey Hinton and Yann Le Cun
  20. 20. Picture Captioning
  21. 21. Translation
  22. 22. January 5, 2017: “After humanity spent thousands of years improving our tactics, computers tell us that humans are completely wrong.” Ke Jie of China
  23. 23. Unsupervised Learning: Generative Adversary Networks
  24. 24. Interfaces Use Case 1980 – Windows 1990 – Browsers 2000 – Apps 2010 – Personal Assistants 2020 – Theory of Mind
  25. 25. BRAIN Initiative Brain Research through Advancing Innovative Neurotechnologies April 2, 2013 April 2, 2013
  26. 26. Geoffrey Hinton Patricia Churchland John Hopfield David Rumelhart Yann LeCun Richard Sutton George Boole Frank Rosenblatt