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TTIC 31230, Fundamentals of Deep Learning David McAllester, April 2017 Generative Adversarial Networks GANs The Generator and The Discriminator A GAN consists of two networks:…

TTIC 31230 Fundamentals of Deep Learning David McAllester April 2017 Architectures and Universality Review: • ηt 0 and ηt→ 0 and ∑ t ηt =∞ implies convergence…

5 Deep Learning • Some Topics in Deep Learning: ∗ Learning algorithms: Back propagation Stochastic Gradient Descent Method Dropout Batch normalization ∗ Generative…

Introduction to Deep Reinforcement Learning 2019 CS420, Machine Learning, Lecture 13 Weinan Zhang Shanghai Jiao Tong University http:wnzhang.net http:wnzhang.netteachingcs420index.html…

Fundamentals - CS 281A: Statistical Learning TheoryYangqing Jia Based on tutorial slides by Lester Mackey and Ariel Kleiner August, 2011 Definition A probability space (,F

Deep Machine Learning Seungjin Choi Department of Computer Science and Engineering Pohang University of Science and Technology 77 Cheongam-ro Nam-gu Pohang 37673 Korea seungjin@postechackr…

Supervised learning Multilayer Perceptron and Deep Learning Some slides are adopted from Honglak Lee Geoffrey Hinton Yann LeCun and MarcAurelio Ranzato Threshold Logic Unit…

1 UVA CS 6316: Machine Learning Lecture 15: Neural Network Deep Learning Basics 3 ewx+b 1 + ewx+b Logistic Regression Sigmoid Function aka logistic logit “S” soft-step…

XFEM-Based Crack Detection Scheme Using a Genetic AlgorithmUnder the supervision of Eli Turkel (TAU) and 2 4 , = 2 , , ∈ Ω, t ∈ (0, ] , 0 = 0 , ∈ Ω

Sanjeev Arora∗ Aditya Bhaskara † Rong Ge‡ Tengyu Ma§ October 24, 2013 Abstract We give algorithms with provable guarantees that learn a class of

1. Deep Learning &Feature LearningMethods for Vision’ 2. Tutorial Overview 3. Overview•–•–––• 4. Existing Recognition Approach•• 5. Motivation••–•…

μVulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection1545-5971 (c) 2019 IEEE. Personal use is permitted, but republication/redistribution

WM CS Zeyi Tim Tao 11012019 Introduction to Deep Learning Optimization Algorithms !1 Topics SGD SGDM AdaGrad Adam AdaDelta RMSprop Adaptive LR ERM problem Statement • Given…

Contributions to deep reinforcement learning and its applications in smartgrids Vincent François-Lavet University of Liege Belgium September 11 2017 160 Motivation 260…

Probabilistic Bayesian deep learning Andreas Damianou Amazon Research Cambridge UK Talk at University of Sheffield 19 March 2019 In this talk Not in this talk: CRFs Boltzmann…

Determinist PG Pathwise deriva2ves Deep Reinforcement Learning and Control Katerina Fragkiadaki Carnegie Mellon School of Computer Science Spring 2020 CMU 10-403 Compu2ng…

Deep Learning DL Frameworks Darknet Keras Deep Learning intro and hands-on tutorial Π passalis@csdauthgr Ε ώ Π ώ Π ΠΘ 1 53 Deep Learning DL Frameworks Darknet Keras…

Provable Bounds for Learning Some Deep Representations Sanjeev Arora∗ Aditya Bhaskara † Rong Ge‡ Tengyu Ma§ October 24 2013 Abstract We give algorithms with provable…

Statistical Learning Theory Part I – 5. Deep Learning Sumio Watanabe Tokyo Institute of Technology Review : Supervised Learning Training Data X1, X2, …, Xn Y1, Y2, …,…