PPT-Greedy Layer-Wise Training of Deep Networks

Author : tatiana-dople | Published Date : 2015-09-29

Yoshua Bengio Pascal Lamblin Dan Popovici Hugo Larochelle NIPS 2007 Presented by Ahmed Hefny Story so far Deep neural nets are more expressive Can learn

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Greedy Layer-Wise Training of Deep Networks: Transcript

Yoshua Bengio Pascal Lamblin Dan Popovici Hugo Larochelle NIPS 2007 Presented by Ahmed Hefny Story so far Deep neural nets are more expressive Can learn wider classes of functions with less . Early Work. Why Deep Learning. Stacked Auto Encoders. Deep Belief Networks. CS 678 – Deep Learning. 1. Deep Learning Overview. Train networks with many layers (vs. shallow nets with just a couple of layers). Carmine . Cerrone. , . Raffaele. . Cerulli. , Bruce Golden. GO IX. Sirmione. , Italy. July 2014. 1. Outline. Motivation. The Minimum Label Spanning Tree (MLST) problem. Experimental justification. Introduction to Carousel . Optimization problems, Greedy Algorithms, Optimal Substructure and Greedy choice. Learning & Development Team. http://academy.telerik.com. . Telerik Software Academy. Table of Contents. Optimization Problems. CIS 606. Spring 2010. Greedy Algorithms. Similar to dynamic programming.. Used for optimization problems.. Idea. When we have a choice to make, make the one that looks best . right now. . Make . a locally . Yuli. Ye . Joint work with Allan Borodin, University of Toronto. Why do we study greedy algorithms? . don’t. A quote from Jeff Erickson’s algorithms book. . Everyone should tattoo the following sentence on the back of their hands, right under all the rules about logarithms and big-Oh notation. The two key components. Optimal Sub-structure. You solve the problem by solving a sub-problem optimally. Greedy Property. Using the choice that seems best at the moment leads to the optimal result. This is tougher to show!. CAP5615 Intro. to Neural Networks. Xingquan (Hill) Zhu. Outline. Multi-layer Neural Networks. Feedforward Neural Networks. FF NN model. Backpropogation (BP) Algorithm. BP rules derivation. Practical Issues of FFNN. Gokarna Sharma (. LSU. ). Brett . Estrade. (. Univ. of Houston. ). Costas Busch (. LSU. ). 1. DISC 2010 - 24th International Symposium on Distributed Computing. Transactional Memory - Background. The emergence of multi-core architectures. . Qiyue Wang. Oct 27, 2017. 1. Outline. Introduction. Experiment setting and dataset. Analysis of activation function. Analysis of gradient. Experiment validation and conclusion . 2. Introduction. Aaron Crandall, 2015. What is Deep Learning?. Architectures with more mathematical . transformations from source to target. Sparse representations. Stacking based learning . approaches. Mor. e focus on handling unlabeled data. trust community . where. security experts share information . and. . work. . together. , . creating. . collaboration. . among. different . e-. infrastructures. Nicole Harris / Alf Moens. Introduction. BY DR.M.MD.MUSTAFA SHARIFF DEPT OF ANATOMY SENIOR LECTURER SRMDC&H DEEP CERVICAL FASCIA • It is also called FASCIA COLLI • The deep cervical fascia of neck is clinically very important for it for and SMA*. Remark: SMA* will be covered by Group Homework Credit Group C’s presentation but not in Dr. . Eick’s. lecture in 2022. Best-first search. Idea: use an . evaluation function. . f(n) . for each node. Eli Gutin. MIT 15.S60. (adapted from 2016 course by Iain Dunning). Goals today. Go over basics of neural nets. Introduce . TensorFlow. Introduce . Deep Learning. Look at key applications. Practice coding in Python.

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