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Introduction to Deep Learning Princeton University COS 495 Instructor: Yingyu Liang What is deep learning? Short answer: recent buzz word Industry Google Facebook Microsoft Musk Toyota Drug Finance

  1. Introduction to Deep Learning Princeton University COS 495 Instructor: Yingyu Liang

  2. What is deep learning? • Short answer: recent buzz word

  3. Industry • Google • Facebook • Microsoft • … • Musk • Toyota • Drug • Finance

  4. Industry • Google

  5. Industry • Facebook

  6. Industry • Microsoft

  7. Industry • Elon Musk

  8. Industry • Toyota

  9. Academy • NIPS 2015: ~4000 attendees, double the number of NIPS 2014

  10. Academy • Science special issue • Nature invited review

  11. What is deep learning? • Longer answer: machine learning framework that shows impressive performance on many Artificial Intelligence tasks

  12. Image • Image classification • 1000 classes Human performance: ~5% Slides from Kaimin He, MSRA

  13. Image • Object location Slides from Kaimin He, MSRA

  14. Image • Image captioning Figure from the paper “ DenseCap : Fully Convolutional Localization Networks for Dense Captioning”, by Justin Johnson, Andrej Karpathy, Li Fei-Fei

  15. Text • Question & Answer Figures from the paper “Ask Me Anything: Dynamic Memory Networks for Natural Language Processing ”, by Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Richard Socher

  16. Game Google DeepMind's Deep Q-learning playing Atari Breakout From the paper “Playing Atari with Deep Reinforcement Learning”, by Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller

  17. Game

  18. The impact • Revival of Artificial Intelligence • Next technology revolution? • A big thing ongoing, should not miss

  19. Questions behind the scene • Return of artificial neural network • What’s different • Why get great performance • Future development • The road to general-purpose AI?

  20. Goal of the course • Introduction • Key concepts • Ticket to the party

  21. Syllabus • Part I: machine learning basics • Linear model, Perceptron, SVM • Multi-class • Training by gradient descent • overfitting • Part II: supervised deep learning (feedforward network) • Part III: unsupervised learning • Part IV: deep learning in the wild

  22. Syllabus • Part I: machine learning basics • Part II: supervised deep learning (feedforward network) • Multiple-layer and Backpropogation • Regularization • Convolution • Part III: unsupervised deep learning • Part IV: deep learning in the wild

  23. Syllabus • Part I: machine learning basics • Part II: supervised deep learning (feedforward network) • Part III: unsupervised deep learning • PCA • Boltzmann machine, Deep Boltzmann machine • autoencoder • Part IV: deep learning in the wild

  24. Syllabus • Part I: machine learning basics • Part II: supervised deep learning (feedforward network) • Part III: unsupervised deep learning • Part IV: deep learning in the wild • Read papers on advanced topics • Play with the code • Presentation

  25. Textbook and materials • Deep Learning: http://www.deeplearningbook.org/ • Suggested software framework: Tensorflow • in Python • Easy to install/use • Can try it on your laptop • Other software frameworks: Theano, Caffe , Torch, Marvin, …

  26. Grading • Problem Sets (5 sets): 70% • Design Projects: 25% • Oral Presentation: 5%

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