PPT-Neural Network Models

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. Kong Da, Xueyu Lei & Paul McKay. Digit Recognition. Convolutional Neural Network. Inspired by the visual cortex. Our example: Handwritten digit recognition. Reference: . LeCun. et al. . Back propagation Applied to Handwritten Zip Code Recognition. 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. 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. . 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. 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. 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. 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. the human mind?. Neural Network Models of Intelligence. Why try to build a mind?. The ultimate test of understanding something . is being able to recreate it. -- . Demis. Hassabis. What . I cannot build I do not truly . 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. . 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. . 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). Lingxiao Ma. . †. , Zhi Yang. . †. , Youshan Miao. ‡. , Jilong Xue. ‡. , Ming Wu. ‡. , Lidong Zhou. ‡. , . Yafei. Dai. . †. †. . Peking University. ‡ . Microsoft Research. USENIX ATC ’19, Renton, WA, USA. Usman Mohseni1, Sai Bargav Muskula2. 1,2Research Scholar, Department of Civil Engineering, IIT Roorkee, Roorkee, INDIA. INTRODUCTION. Rainfall-runoff modelling is one of the most prominent hydrological models used to examine the relation between rainfall and runoff .