PPT-Neural Network Theory

Table of Contents Part 1 The Motivation and History of Neural Networks Part 2 Components of Artificial Neural Networks Part 3 Particular Types of Neural Network Architectures Part 4 Fundamentals on Learning and Training Samples. ReNN. ). A . New Alternative . for Data-driven . Modelling . in . Hydrology . and Water . Resources Engineering. Saman Razavi. 1. , Bryan Tolson. 1. , Donald Burn. 1. , and Frank Seglenieks. 2. . 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. Shuochao Yao, Yiwen Xu, Daniel Calzada. Network Compression and Speedup. 1. Source: . http://isca2016.eecs.umich.edu/. wp. -content/uploads/2016/07/4A-1.pdf. Network Compression and Speedup. 2. Why smaller models?. 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. Week 5. Applications. Predict the taste of Coors beer as a function of its chemical composition. What are Artificial Neural Networks? . Artificial Intelligence (AI) Technique. Artificial . Neural Networks. Ashutosh. Pandey and . Shashank. . S. rikant. Layout of talk. Classification problem. Idea of gradient descent . Neural network architecture. Learning a function using neural network. Backpropagation algorithm. Recurrent Neural Network Cell. Recurrent Neural Networks (unenrolled). LSTMs, Bi-LSTMs, Stacked Bi-LSTMs. Today. Recurrent Neural Network Cell.  .  .  .  . Recurrent Neural Network Cell.  .  .  . E . Oznergiz. , C . Ozsoy. I . Delice. , and A . Kural. Jed Goodell. September 9. th. ,2009. Introduction. A fast, reliable, and accurate mathematical model is needed to predict the rolling force, torque and exit temperature in the rolling process. . dendrites. cell body. axon. signal. direction. colaterals. synapse. . . .. Biological Neural Networks. electrical. signal. electrical. signal. synaptic. gap. neurotransmitters. dendrite. vesicles. presynaptic. Daniel Boonzaaier. Supervisor – Adiel Ismail. April 2017. Content. Project Overview. Checkers – the board game. Background on Neural Networks. Neural Network applied to Checkers. Requirements. Project Plan. 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. Roi. . Livni. , Shai . Shalev-Shwartz. . Ohad. Shamir. Remainder on neural networks. Neural network = A direct graph (usually acyclic) where each vertex corresponds to a neuron.. A Neuron = A weighted sum of its predecessor neurons + activation function . 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. Dr David Wong. (with thanks to Dr . Gari. Clifford, G.I.T). Overview. What are Artificial Neural Networks (ANNs)?. How do you construct them?. Choosing architecture. Pruning. How do you train them?.