What are examples of domain knowledge default policies in Go? The study of their numerical Focus in Chapter 3 on getting used to the notation we’ll use throughout the module, and an introduction to the Bellman operator and fixed point equations. Motivation: How do you reason about games? Why must every game have a Pareto Optimal strategy? %bkD*�d/n�� Why does definition 3.3.12 imply that the vertices of a simplex must all receive different labels? Motivation: The introduction of Residual Networks (ResNets) made possible to train very deep networks. Work fast with our official CLI. If nothing happens, download the GitHub extension for Visual Studio and try again. Can you prove Theorem 3.1.8 (which is a really cool result)? Runge-Kutta methods: Section 11.8 from NM or Sections 12. We then look into how ResNets approximates ODEs and how this interpretation can motivate neural net architectures and new training approaches. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. ? Besides the lectures, we will also run a parallel curricula (optional), following the Depth First Learning methodology. The detailed abstract of the project will be graded at the mid-term. Optional Reading: The two below are CFR extensions used in DeepStack. Including 6.5 is an introduction to Q-learning in practice, updating one state-action pair at a time (without worrying about function approximation yet). (Knuth) Show that AlphaBetaMin(p, alpha, beta) = -AlphaBetaMax(p, -beta, -alpha). Motivation: In the previous class we introduced some simple schemes to numerically solve ODEs. Non-Euclidean Geometry: Graph Neural Networks. (LT), Prove that in a zero-sum game, the expected payoff to each player is the same for every equilibrium. A Course on Mathematical Theories of Deep Learning. This Graduate-level topics course aims at offering a glimpse into the emerging mathematical questions around Deep Learning. This is used for density estimation and generative modeling, and it is another model which can be seen a time-discretization of its continuous-time counterpart. Sutton: Sections 2.1 - 2.6 (Find on newclasses.nyu.edu in the class materials). Tuesdays from 7.10pm-9pm. Deep Learning usually refers to a set of computational models, composed of multiple processing layers, that perform tasks on data by generating multiple intermediate representations. MAS Sections 3.3, 3.4.5, 3.4.7, 4.1, 4.2.4, 4.3, 4.6. (p. 305). Office Hours: Tuesdays 4:30pm-6:00pm, office 612, 60 5th ave. Lecture Instructor: Joan Bruna (bruna@cims.nyu.edu), Tutor (Parallel Curricula): Luca Venturi (lv800@nyu.edu), Tutor (Parallel Curricula): Aaron Zweig (az831@nyu.edu). Landscape of Deep Learning Optimization (Tensor/Matrix factorization, Deep Nets; open problems). T��F�N$�l}1u6� ����\J�7��PLp�)�MK���KmȻ�:/���Z���� ���NDߵ]��"�|��W͖�G��W��; ��h�N��2Q���LYHx�����.�;�/�m�39P��3�87�2��]�z�R' >����ba�$�!v��F$p铸�W�h=�4�Hu���J�+��T% `C��PUb)G�6 ���f�7?�vPJ�asRdw�5`7�P�p�3��l .�u�tR��b�%�OYh>nJݱ��!�����1X�{"{�Ҧu���a�-e��(��|ȑ{|�� A Course on Mathematical Theories of Deep Learning. Succinctly describe the technique demonstrated in the Accelerating Best Response paper. Do you understand “Continual Re-solving”? Tutoring Session with Parallel Curricula (optional): Fridays 11am-12:15pm CIWW 101. In particular, we will focus on the different geometrical aspects surounding these models, from input geometric stability priors to the geometry of optimization, generalisation and learning. Yann LeCun, a professor at New York University’s Courant Institute of Mathematical Sciences, has been awarded the ACM A.M. Turing Award for his breakthroughs in artificial intelligence and, specifically, deep learning and convolutional neural networks—the foundation of modern computer vision, speech recognition, speech synthesis, image synthesis, and natural language processing. ): Prove (13.7) using the definitions and elementary calculus. Take your favorite normal-form game, find a Nash Equilibrium, and then find a corresponding Correlated Equilibrium. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Introduction: the Curse of Dimensionality. Consider the problem of optimizing a real-valued function g over the solution of the ODE y’ = Ay , y(0) = y_0 at time T>0: min_{y0, A} g(y(T)). In this class we go through some more involved schemes and their convergence analysis. What were the differences between “Mastering the Game of Go …” and “Thinking Fast and Slow …”? How do the authors of (Multi-level […]) explain the phenomena of still having almost as good performances in residual networks when removing a layer? These collaborations provide you with opportunities to draw on content knowledge and pedagogical expertise from a diverse range of sources. Motivation: In this class we take a little detour through the topic of Normalizing Flows. /Length 2673 What is the computational complexity of evaluating a determinant of a N x N matrix, and why is that relevant in this context? Tutoring Session with Parallel Curricula (optional): Fridays 11am-12:15pm CIWW 101. We use essential cookies to perform essential website functions, e.g. We will also review the difference between supervised and reinforcement learning - and overview how Deep Learning can be used to act on the world and learn from that information. (2), why is it that p’s successor position is of type 3 if p is not terminal? Why or why not? will culminate with Blackwell’s Approachability Theorem, a generalization of

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