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如果你不打算参加该课程，但对课程的讲座嘉宾感兴趣，或者对伯克利有关深度学习的演讲感兴趣，请在以下论坛注册以收取今后的讲座通知邮件：https://groups.google.com/forum/#!forum/berkeley-deep-learning 课程邮箱：[email protected] 具体课程表 Lecture 1 8/31/16
主要阅读材料： Deep Learning, by Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Lecture 2 9/7/16 : Why does deep learning actually work? 主要阅读材料： Explaining and Harnessing Adversarial Examples, by Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Qualitatively Characterizing Neural Network Optimization Problems, by Ian J. Goodfellow, Oriol Vinyals, and Andrew M. Saxe. 背景阅读： Algorithmic probability, Wikipedia Does Algorithmic Probability Solve the Problem of Induction?, by Ray Solomonoff A Theory of the Learnable, by Leslie Valiant On Small Depth Threshold Circuits, by Alexander A. Razborov (read table on last page) Lecture 3 9/14/16 Wojciech Zaremba: Turing-complete neural-network based models 主要阅读材料： Extensions and Limitations of the Neural GPU, by Eric Price, Wojciech Zaremba, and Ilya Sutskever Learning Simple Algorithms from Examples, by Wojciech Zaremba, Tomas Mikolov, Armand Joulin, and Rob Fergus 背景阅读： Learning Efficient Algorithms with Hierarchical Attentive Memory, by Marcin Andrychowicz and Karol Kurach Neural Random-Access Machines, by Karol Kurach, Marcin Andrychowicz, and Ilya Sutskever Neural Programmer-Interpreters, by Scott Reed and Nando de Freitas Lecture 4 9/21/16 Kunal Talwar: Deep Learning with Differential Privacy 主要阅读材料： Deep Learning with Differential Privacy by Abadi, Chu, Goofellow, McMahan, Mironov, Talwar and Zhang Chapters 1 and 2 of The Algorithmic Foundations of Differential Privacy by Cynthia Dwork and Aaron Roth 背景阅读： Chapter 3 of The Algorithmic Foundations of Differential Privacy Differentially Private Empirical Risk Minimization by Bassily, Smith and Thakurta (Sections 1 and 2) Analyze Gauss: Optimal bounds for privacy-preserving PCA by Dwork, Talwar, Thakurta and Zhang Lecture 5 9/27/16 (Special date)
Location: Wozniak Lounge (430-8 Soda Hall) Time: 12:30 pm Lecture 6?? 9/28/16 Samy Bengio: Sequence models 主要阅读材料： Order Matters: Sequence to sequence for sets, by Oriol Vinyals, Samy Bengio, and Manjunath Kudlur Reward Augmented Maximum Likelihood for Neural Structured Prediction, by Mohammad Norouzi, Samy Bengio, Zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, and Dale Schuurmans 背景阅读： Sequence to Sequence Learning with Neural Networks, by Ilya Sutskever, Oriol Vinyals, and Quoc V. Le Pointer Networks, by Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly (责任编辑：本港台直播) |