PPT-CS 6501: Deep Learning

for Computer Graphics Training Neural Networks II Connelly Barnes Overview Preprocessing Initialization Vanishingexploding gradients problem Batch normalization Dropout Additional neuron types. Hongning Wang. CS@UVa. Today’s lecture. k. -means clustering . A typical . partitional. . clustering . algorithm. Convergence property. Expectation Maximization algorithm. Gaussian mixture model. . Professor Qiang Yang. Outline. Introduction. Supervised Learning. Convolutional Neural Network. Sequence Modelling: RNN and its extensions. Unsupervised Learning. Autoencoder. Stacked . Denoising. . Carey . Nachenberg. Deep Learning for Dummies (Like me) – Carey . Nachenberg. (Like me). The Goal of this Talk?. Deep Learning for Dummies (Like me) – Carey . Nachenberg. 2. To provide you with . for. Computer Graphics. Basics of Machine Learning. Connelly Barnes. Overview. Supervised, unsupervised, and reinforcement learning. Simple learning models. Clustering. Linear . regression. Linear Support Vector Machines (SVM). How to represent a document. Represent by a string?. No semantic meaning. Represent by a list of sentences?. Sentence is just like a short document (recursive definition). CS@UVa. CS 6501: Text Mining. 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 . Neural Networks from Scratch. Presented . By. Wasi Uddin . Ahmad. 3. rd. November, 2016. Written By. Denny . Britz. http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/. "Lane, Mary E. . Hongning Wang. Congratulations. Job . Offer from Bing Core Ranking team. Design the ranking module for Bing.com. CS 6501: Information Retrieval. 2. CS@UVa. How should I rank documents?. Answer: Rank by relevance!. The Future of Real-Time Rendering?. 1. Deep Learning is Changing the Way We Do Graphics. [Chaitanya17]. [Dahm17]. [Laine17]. [Holden17]. [Karras17]. [Nalbach17]. Video. “. Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion”. Presenter : Jingyun Ning. “CVPR 2016 Best Paper Award”. Introduction. Deep Residual Networks (ResNets). A simple and clean framework of training “very” deep nets. State-of-the-art performance for. Lecture 2: N-gram Kai-Wei Chang CS @ University of Virginia kw@kwchang.net Couse webpage: http://kwchang.net/teaching/NLP16 1 CS 6501: Natural Language Processing This lecture Language Models What are N-gram models? CS@UVa. Today’s lecture. Support vector machines. Max margin classifier. Derivation of linear SVM. Binary and multi-class cases. Different types of losses in discriminative models. Kernel method. Non-linear SVM. Garima Lalwani Karan Ganju Unnat Jain. Today’s takeaways. Bonus RL recap. Functional Approximation. Deep Q Network. Double Deep Q Network. Dueling Networks. Recurrent DQN. Solving “Doom”. Assistant Professor. Computer Science and Engineering Department. Indian Institute of Technology Kharagpur. http://cse.iitkgp.ac.in/~adas/. Biological Neural Network. Image courtesy: F. . A. . Makinde.