PPT-Multi-Layer Feedforward Neural Networks

CAP5615 Intro to Neural Networks Xingquan Hill Zhu Outline Multilayer Neural Networks Feedforward Neural Networks FF NN model Backpropogation BP Algorithm BP rules derivation Practical Issues of FFNN. Cost function. Machine Learning. Neural Network (Classification). Binary classification. . . 1 output unit. Layer 1. Layer 2. Layer 3. Layer 4. Multi-class classification . (K classes). K output units. Machine . Learning. 1. Last Time. Perceptrons. Perceptron. Loss vs. Logistic Regression Loss. Training . Perceptrons. and Logistic Regression Models using Gradient Descent. 2. Today. Multilayer Neural Networks. What are Artificial Neural Networks (ANN)?. ". Colored. neural network" by Glosser.ca - Own work, Derivative of File:Artificial neural . network.svg. . Licensed under CC BY-SA 3.0 via Commons - https://commons.wikimedia.org/wiki/File:Colored_neural_network.svg#/media/File:Colored_neural_network.svg. support vector machines, conditional random . fields,. NEURAL networks. Heng. . Ji. jih@rpi.edu. 04/12, 04/15, 2016. 2. Maximum Entropy. 3. Maximum Entropy is a technique for learning probability distributions from data. (sometimes called “Multilayer . Perceptrons. ” or MLPs). Linear . s. eparability. Feature 1. Feature 2. Hyperplane. In . 2D: . A perceptron can separate data that is linearly separable.. A bit of history. 2015/10/02. 陳柏任. Outline. Neural Networks. Convolutional Neural Networks. Some famous CNN structure. Applications. Toolkit. Conclusion. Reference. 2. Outline. Neural Networks. Convolutional Neural Networks. Perceptron. x. 1. x. 2. x. D. w. 1. w. 2. w. 3. x. 3. w. D. Input. Weights. .. .. .. Output:. . sgn. (. w. x. . b). Can incorporate bias as component of the weight vector by always including a feature with value set to 1. . 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). Dr David Wong. (With thanks to Dr Gari Clifford, G.I.T). The Multi-Layer Perceptron. single layer can only deal with linearly separable data. Composed of many connected neurons . Three general layers; . Developing efficient deep neural networks. Forrest Iandola. 1. , Albert Shaw. 2. , Ravi Krishna. 3. , Kurt Keutzer. 4. 1. UC Berkeley → DeepScale → Tesla → Independent Researcher. 2. Georgia Tech → DeepScale → Tesla. Short-Term . Memory. Recurrent . Neural Networks. Meysam. . Golmohammadi. meysam@temple.edu. Neural Engineering Data Consortium. College . of Engineering . Temple University . February . 2016. Introduction. Learn to build neural network from scratch.. Focus on multi-level feedforward neural networks (multi-level . perceptrons. ). Training large neural networks is one of the most important workload in large scale parallel and distributed systems. Mark Hasegawa-Johnson. April 6, 2020. License: CC-BY 4.0. You may remix or redistribute if you cite the source.. Outline. Why use more than one layer?. Biological inspiration. Representational power: the XOR function.