PPT-Visualizing and Understanding recurrent Neural Networks

Presented By Collin Watts Wrritten By Andrej Karpathy Justin Johnson Li Feifei Plan Of Attack What were going to cover Overview Some Definitions Expiremental Analysis Lots of Results. Presented by . Vinoth. . Kulaveerasingam. QMUL ELE021/ELED021/ELEM021 • 5 Mar 2012. Paper by . Jorg. . Langner. and Werner . Goebl. Visualizing Expressive Performance in Tempo-Loudness Space. What is the aim?. 1. Recurrent Networks. Some problems require previous history/context in order to be able to give proper output (speech recognition, stock forecasting, target tracking, etc.. One way to do that is to just provide all the necessary context in one "snap-shot" and use standard learning. Deep Learning @ . UvA. UVA Deep Learning COURSE - Efstratios Gavves & Max Welling. LEARNING WITH NEURAL NETWORKS . - . PAGE . 1. Machine Learning Paradigm for Neural Networks. The Backpropagation algorithm for learning with a neural network. Abhishek Narwekar, Anusri Pampari. CS 598: Deep Learning and Recognition, Fall 2016. Lecture Outline. Introduction. Learning Long Term Dependencies. Regularization. Visualization for RNNs. Section 1: Introduction. Recurrent Neural Network Cell. Recurrent Neural Networks (unenrolled). LSTMs, Bi-LSTMs, Stacked Bi-LSTMs. Today. Recurrent Neural Network Cell.  .  .  .  . Recurrent Neural Network Cell.  .  .  . Nitish Gupta, Shreya Rajpal. 25. th. April, 2017. 1. Story Comprehension. 2. Joe went to the kitchen. Fred went to the kitchen. Joe picked up the milk. Joe travelled to his office. Joe left the milk. Joe went to the bathroom. . 1. Table of contents. Recurrent models. Partially recurrent neural networks. . Elman networks. Jordan networks. Recurrent neural networks. BackPropagation Through Time. Dynamics of a neuron with feedback. Dongwoo Lee. University of Illinois at Chicago . CSUN (Complex and Sustainable Urban Networks Laboratory). Contents. Concept. Data . Methodologies. Analytical Process. Results. Limitations and Conclusion. 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). Dr. Abdul Basit. Lecture No. 1. Course . Contents. Introduction and Review. Learning Processes. Single & Multi-layer . Perceptrons. Radial Basis Function Networks. Support Vector and Committee Machines. Applications. Lectures . 14-15: . CNN. . and . RNN Details. Zhu Han. University of Houston. Thanks . Xusheng. Du and Kevin Tsai For Slide Preparation. 1. CNN outline. The convolution operation. Motivation. Abigail See, Peter J. Liu, Christopher D. Manning. Presented by: Matan . Eyal. Agenda. Introduction. Word Embeddings. RNNs. Sequence-to-Sequence. Attention. Pointer Networks. Coverage Mechanism. Introduction . Introduction. Dynamic networks. are networks . that. contain . delays. (or . integrators, for continuous-time networks. ) and that . operate on a sequence of inputs. . . In other words, . the ordering of the inputs is important. Models and applications. Outline. Sequence Data. Recurrent Neural Networks Variants. Handling Long Term Dependencies. Attention Mechanisms. Properties of RNNs. Applications of RNNs. Hands-on LSTM-supported timeseries prediction.