PPT-Deep Learning for Sequential Data

Models and applications Outline Sequence Data Recurrent Neural Networks Variants Handling Long Term Dependencies Attention Mechanisms Properties of RNNs Applications of RNNs Handson LSTMsupported timeseries prediction. BA 445 Lesson B.3 Sequential Quantity Competition. I love . going down to the elementary school, watching all the kids jump and shout, but they don’t know I’m using blanks. . ~ Jack . Handey. .. 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. Continuous. Scoring in Practical Applications. Tuesday 6/28/2016. By Greg Makowski. Greg@Ligadata.com. www.Linkedin.com/in/GregMakowski. Community @. . http. ://. Kamanja.org. . . Try out. Future . Applications. Lectures 11-12: Deep Learning Basics. Zhu Han. University of Houston. Thanks for Dr. . Hien. Nguyen slides and help by . Xunshen. Du and Kevin Tsai. 1. outline. Motivation and overview. CS 501:CS Seminar. Min Xian. Assistant Professor. Department of Computer Science. University of Idaho. Image from NVIDIA. Researchers:. Geoff Hinton. Yann . LeCun. Andrew Ng. Yoshua. . Bengio. …. Prabhat. Data Day. August 22, 2016. Roadmap. Why you should care about Machine Learning?. Trends in Industry. Trends in Science . What is Machine Learning?. Taxonomy. Methods. Tools (Evan . Racah. ). 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. Big . Data and Deep Learning. Big Data seminar. Presentation 10.14.15. Outline. Emotiv. demo. Data Acquisition. Cognitive models for emotions recognition. Big Data. Deep Learning. . Human Brain: the Big Data model. Google. Pierre. Sermanet,. Google. Dumitru. Erhan,. Google. Wei. Liu,. UNC. Yangqing. Jia,. Google. Scott. Reed,. University of Michigan. Dragomir. Anguelov,. Google. Vincent. Vanhoucke,. Google. Andrew. Algorithms and Application s Xuyu Wang, Auburn University Abstract: With the rapid growth of mass data , how to intelligently proc ess these big data and extract valuable information from hug Ryota Tomioka (. ryoto@microsoft.com. ). MSR Summer School. 2 July 2018. Azure . iPython. Notebook. https://notebooks.azure.com/ryotat/libraries/DLTutorial. Agenda. This lecture covers. Introduction to machine learning. Deep Q-learning. Instructor: Guni Sharon. 1. CSCE-689, Reinforcement Learning. Stateless decision process. Markov decision process. Solving MDPs (offline). Dynamic programming . Monte-Carlo. Temporal difference. 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. 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.