PPT-Greedy Layer-Wise Training of Deep Networks

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). 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. Submitted by: Supervised by:. Ankit. . Bhutani. Prof. . Amitabha. . Mukerjee. (Y9227094) Prof. K S . Venkatesh. AUTOENCODERS. AUTO-ASSOCIATIVE NEURAL NETWORKS. OUTPUT SIMILAR AS INPUT. DIMENSIONALITY REDUCTION. Neural . Network Architectures:. f. rom . LeNet. to ResNet. Lana Lazebnik. Figure source: A. . Karpathy. What happened to my field?. . Classification:. . ImageNet. Challenge top-5 error. Figure source: . Presenter: . Yanming. . Guo. Adviser: Dr. Michael S. Lew. Deep learning. Human. Computer. 1:4. Human . v.s. . Computer. Deep learning. Human. Computer. 1:4. Human . v.s. . Computer. Deep Learning. Why better?. cs543/. ece549 Spring 2016. Due date: Wednesday, May 4, 11:59:59PM. Prepared with the help of . Chih-Hui. Ho . Platform. Kaggle in class. Create an account. Click on . invitation. Then you . will be added . Deep Neural Networks . Huan Sun. Dept. of Computer Science, UCSB. March 12. th. , 2012. Major Area Examination. Committee. Prof. . Xifeng. . Yan. Prof. . Linda . Petzold. Prof. . Ambuj. Singh. Reading and Research in Deep Learning. James K Baker. 11-364 Deep Learning R&R. Hands-on Tutorial Books with Sample Code. Leading Edge Research Papers. Background Tasks. Learn the core concepts of Deep Learning. . 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. Introduction to Back Propagation Neural . Networks BPNN. By KH Wong. Neural Networks Ch9. , ver. 8d. 1. Introduction. Neural Network research is are very . hot. . A high performance Classifier (multi-class). Big . Data and Deep Learning. Big Data seminar. Presentation 10.14.15. Outline. Emotiv. demo. Data Acquisition. Cognitive models for emotions recognition. Big Data. Deep Learning. . Human Brain: the Big Data model. Short-Term . Memory. Recurrent . Neural Networks. Meysam. . Golmohammadi. meysam@temple.edu. Neural Engineering Data Consortium. College . of Engineering . Temple University . February . 2016. Introduction. networks with many layers . (vs. shallow nets with just a couple of layers). Multiple layers work to build an improved feature space. First layer learns 1. st. order features (e.g. edges…). 2. nd. 1. Deep Learning. Early Work. Why Deep Learning. Stacked Auto Encoders. Deep Belief Networks. Deep Learning Overview. Train networks with many layers (vs. shallow nets with just a couple of layers). Multiple layers work to build an improved feature space.