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Topic · convolutional

convolutional

The 35 most recent episodes and tracks on this topic.

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  1. Lecture 13 | (2/5) Recurrent Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
  2. Lecture 12 | (1/5) Recurrent Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
  3. Lecture 11 | (3/3) Convolutional Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
  4. Lecture 10 | (2/3) Convolutional Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
  5. Lecture 8 | Batch Normalization, Dropout and other Regularization methods(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
  6. Lecture 9 | (1/3) Convolutional Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
  7. Lecture 7 | Acceleration, Regularization, and Normalization(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
  8. Lecture 6 | Convergence, Loss Surfaces, and Optimization(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
  9. The Neural Basis of Vision Through Convolutional Neural Networks by Mike Tarr(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
  10. Lecture 5 | Convergence, Learning Rates, and Gradient Descent(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
  11. (Old) Lecture 9 | (2/3) Convolutional Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
  12. (Old) Lecture 15 | (3/3) Recurrent Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
  13. Continual Learning in Neural Networks by Pulkit Agarwal(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
  14. (Old) Lecture 11 | (1/3) Recurrent Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
  15. Cascade-Correlation and Deep Learning by Scott Fahlman (Spring 2019)(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
  16. (Old) Lecture 12 | (2/3) Recurrent Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
  17. (Old) Lecture 10 | (3/3) Convolutional Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
  18. (Old) Lecture 8 | (1/3) Convolutional Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
  19. (Old) Lecture 7 | Optimization and Generalization(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
  20. (Old) Lecture 6 | Acceleration, Regularization, and Normalization(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
  21. Lecture 15 | Efficient Methods and Hardware for Deep LearningLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
  22. Lecture 14 | Deep Reinforcement LearningLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
  23. Lecture 13 | Generative ModelsLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
  24. Lecture 12 | Visualizing and UnderstandingLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
  25. Lecture 11 | Detection and SegmentationLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
  26. Lecture 9 | CNN ArchitecturesLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
  27. Lecture 10 | Recurrent Neural NetworksLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
  28. Lecture 8 | Deep Learning SoftwareLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
  29. Lecture 7 | Training Neural Networks IILecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
  30. Lecture 6 | Training Neural Networks ILecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
  31. Lecture 5 | Convolutional Neural NetworksLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
  32. Lecture 4 | Introduction to Neural NetworksLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
  33. Lecture 3 | Loss Functions and OptimizationLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
  34. Lecture 2 | Image ClassificationLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
  35. Lecture 1 | Introduction to Convolutional Neural Networks for Visual RecognitionLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes