딥러닝 특론 (Selected topics in Deep learning)
- 강의실: 공학 x관 xxx호
- Textbooks
- I. Goodfellow, Y. Bengio, A. Courville, Deep learning, MIT Press, 2016 [book]
- Kevin Patrick Murphy, Machine Learning: a Probabilistic Perspective, MIT Press, 2012 [link][pdf] [pdf2]
- Carl Edward Rasmussen and Christopher K. I. Williams, Gaussian Processes for Machine Learning, MIT Press, 2006 [link][pdf]
- D. Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012 [link][pdf]
- Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007 [link]
- References
- Martin J. Wainwright1 and Michael I. Jordan, Graphical Models, Exponential Families, and Variational Inference, Foundations and Trends in Machine Learning, 2009 [pdf]
- U Toronto's ML lecture: link
- Mathematics summary sheet [pdf]
- Matrix differential calculus with applications in statistics and econometrics pdf
- Python code for probabilistic machine learning [link]
- Edward: A library for probabilistic modeling, inference, and criticism [link]
- Tensorflow Distributions pdf
- Z. Ghahramani, Probabilistic machine learning and artificial intelligence, Nature '15 [pdf]
- Lecture 0: Introduction [pdf]
- Lecture 1: Introduction to deep learning [pdf]
- Read the book of "Deep learning", MIT press
- Lecture 2: Meta learning [pdf]
- Lecture 3: Automated deep learning [pdf]
- Lecture 4: Disentangled representation learning [pdf]
- Lecture 5: Neuro-symbolic models [pdf]
- Lecture 6: Explainable AI for deep learning
- Lecture 7: Language and vision