PPT-Lecture 1: Deep Neural Networks and Backpropagation Training

Reading and Research in Deep Learning James K Baker 11364 Deep Learning RampR Handson Tutorial Books with Sample Code Leading Edge Research Papers Background Tasks Learn the core concepts of Deep Learning. 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. 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. 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. ISHAY BE’ERY. ELAD KNOLL. OUTLINES. . Motivation. Model . c. ompression: mimicking large networks:. FITNETS : HINTS FOR THIN DEEP NETS . (A. Romero, 2014). DO DEEP NETS REALLY NEED TO BE DEEP . (Rich Caruana & Lei Jimmy Ba 2014). Deep Neural Networks . Huan Sun. Dept. of Computer Science, UCSB. March 12. th. , 2012. Major Area Examination. Committee. Prof. . Xifeng. . Yan. Prof. . Linda . Petzold. Prof. . Ambuj. Singh. 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!. Lecture . 15. October 19, 2016. School of Computer Science. Readings:. Bishop . Ch. . 5. Murphy Ch. 16.5, Ch. 28. Mitchell Ch. 4. 10-601B Introduction to Machine Learning. Reminders. 2. Outline. Logistic Regression (Recap). 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. Zachary . C. Lipton . zlipton@cs.ucsd.edu. Time. . series. Definition. ：. A.  time series is a series of . data. . points.  indexed (or listed or graphed) in time order. . It . is a sequence of . Part 1. About me. Or Nachmias. No previous experience in neural networks. Responsible to show the 2. nd. most important lecture in the seminar.. References. Stanford CS231: Convolution Neural Networks for Visual Recognition . Topics: 1. st. lecture wrap-up, difficulty training deep networks,. image classification problem, using convolutions,. tricks to train deep networks . . Resources: http://www.cs.utah.edu/~rajeev/cs7960/notes/ . 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. 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.