Introduction to Deep Learning Princeton University COS 495 - PowerPoint PPT Presentation

Sep 24, 2022 •22 likes •282 views

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

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
Industry • Google
Industry • Facebook
Industry • Microsoft
Industry • Elon Musk
Industry • Toyota
Academy • NIPS 2015: ~4000 attendees, double the number of NIPS 2014
Academy • Science special issue • Nature invited review
What is deep learning? • Longer answer: machine learning framework that shows impressive performance on many Artificial Intelligence tasks
Image • Image classification • 1000 classes Human performance: ~5% Slides from Kaimin He, MSRA
Image • Object location Slides from Kaimin He, MSRA
Image • Image captioning Figure from the paper “ DenseCap : Fully Convolutional Localization Networks for Dense Captioning”, by Justin Johnson, Andrej Karpathy, Li Fei-Fei
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
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
Game
The impact • Revival of Artificial Intelligence • Next technology revolution? • A big thing ongoing, should not miss
Questions behind the scene • Return of artificial neural network • What’s different • Why get great performance • Future development • The road to general-purpose AI?
Goal of the course • Introduction • Key concepts • Ticket to the party
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
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
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
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
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, …
Grading • Problem Sets (5 sets): 70% • Design Projects: 25% • Oral Presentation: 5%

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