PPT-Neural I maging and Signal Systems

BT 640 Navin Gupta Assistant Professor Dept of BSBE IIT Guwahati Email cnguptaiitgernetin Few images are from Google What do we know about Brain Orientations to Look at Any Idea. 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. Banafsheh. . Rekabdar. Biological Neuron:. The Elementary Processing Unit of the Brain. Biological Neuron:. A Generic Structure. Dendrite. Soma. Synapse. Axon. Axon Terminal. Biological Neuron – Computational Intelligence Approach:. Brains and games. Introduction. Spiking Neural Networks are a variation of traditional NNs that attempt to increase the realism of the simulations done. They more closely resemble the way brains actually operate. 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. and parallel corpus generation. Ekansh. Gupta. Rohit. Gupta. Advantages of Neural Machine Translation Models. Require . only a fraction of the memory needed by traditional statistical machine translation (SMT) . 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. 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. By, . . Sruthi. . Moola. Convolution. . Convolution is a common image processing technique that changes the intensities of a pixel to reflect the intensities of the surrounding pixels. A common use of convolution is to create image filters. Recurrent Neural Network Cell. Recurrent Neural Networks (unenrolled). LSTMs, Bi-LSTMs, Stacked Bi-LSTMs. Today. Recurrent Neural Network Cell.  .  .  .  . Recurrent Neural Network Cell.  .  .  . 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. 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 2. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. Hinton’s Brief History of Machine Learning. What was hot in 1987?. Pero-System Imaging Frames: Model 351 NT and Accessories Eli Gutin. MIT 15.S60. (adapted from 2016 course by Iain Dunning). Goals today. Go over basics of neural nets. Introduce . TensorFlow. Introduce . Deep Learning. Look at key applications. Practice coding in Python.