PPT-Neural Networks and Deep Learning

Eli Gutin MIT 15S60 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. Aaron Crandall, 2015. What is Deep Learning?. Architectures with more mathematical . transformations from source to target. Sparse representations. Stacking based learning . approaches. Mor. e focus on handling unlabeled data. 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. 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. 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. Deep . Learning. James K . Baker, Bhiksha Raj. , Rita Singh. Opportunities in Machine Learning. Great . advances are being made in machine learning. Artificial Intelligence. Machine. Learning. After decades of intermittent progress, some applications are beginning to demonstrate human-level performance!. Ali Cole. Charly. . Mccown. Madison . Kutchey. Xavier . henes. Definition. A directed network based on the structure of connections within an organism's brain. Many inputs and only a couple outputs. Fall 2018/19. 7. Recurrent Neural Networks. (Some figures adapted from . NNDL book. ). Recurrent Neural Networks. Noriko Tomuro. 2. Recurrent Neural Networks (RNNs). RNN Training. Loss Minimization. Bidirectional RNNs. Weifeng Li, . Victor Benjamin, Xiao . Liu, and . Hsinchun . Chen. University of Arizona. 1. Acknowledgements. Many of the pictures, results, and other materials are taken from:. Aarti. Singh, Carnegie Mellon University. Secada combs | bus-550. AI Superpowers: china, silicon valley, and the new world order. Kai Fu Lee. Author of AI Superpowers. Currently Chairman and CEO of . Sinovation. Ventures and President of . Sinovation. New-Generation Models & Methodology for Advancing . Speech Technology . and Information Processing. Li Deng . Microsoft Research, Redmond, . USA. CCF, . Beijing. , July . 8. , 2013. (including joint work with colleagues at MSR, U of Toronto, etc.) . Outline. What is Deep Learning. Tensors: Data Structures for Deep Learning. Multilayer Perceptron. Activation Functions for Deep Learning. Model Training in Deep Learning. Regularization for Deep Learning. 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.