PPT-CS 7643: Deep Learning Dhruv Batra

Georgia Tech Topics Toeplitz matrices and convolutions matrix mult Dilateda trous convolutions Backprop in conv layers Transposed convolutions Administrativia HW1 extension . Adam Coates. Stanford University. (Visiting Scholar: Indiana University, Bloomington). What do we want ML to do?. Given image, predict complex high-level patterns:. Object recognition. Detection. Segmentation. Quoc V. Le. Stanford University and Google. Purely supervised. Quoc V. . Le. Almost abandoned between 2000-2006. - . Overfitting. , slow, many local minima, gradient vanishing. In 2006, Hinton, et. al. proposed RBMs to . Early Work. Why Deep Learning. Stacked Auto Encoders. Deep Belief Networks. CS 678 – Deep Learning. 1. Deep Learning Overview. Train networks with many layers (vs. shallow nets with just a couple of layers). 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. With Fabric. http://docs.fabfile.org. Varun Batra @ Deft Infotech Pvt. Ltd.. I am Lazy!. I need one command to deploy codes.. >fab deploy. That’s what I was talking about . . Varun Batra @ Deft Infotech Pvt. Ltd.. 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 . 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”. Larry . Zitnick. (Facebook AI Research). Devi Parikh. (Virginia Tech). Stanislaw . Antol. (Virginia Tech). Aishwarya Agrawal. (Virginia Tech). Overview of Challenge . Outline. Overview of Task and Dataset . Aaron Schumacher. Data Science DC. 2017-11-14. Aaron Schumacher. planspace.org has these slides. Plan. applications. : . what. t. heory. applications. : . how. onward. a. pplications: what. Backgammon. 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. Topic 3. 4/15/2014. Huy V. Nguyen. 1. outline. Deep learning overview. Deep v. shallow architectures. Representation learning. Breakthroughs. Learning principle: greedy layer-wise training. Tera. . scale: data, model, . Machine Learning. Dhruv Batra . Virginia Tech. Topics: . Supervised Learning. General Setup, learning from data. Nearest . Neighbour. Readings. : . Barber 14 (. kNN. ) . Administrativia. New class room. ,. 10-708 Recitation. 10. /30/. 2008. Contents. MRFs. Semantics / Comparisons with . BNs. Applications to vision. HW4 implementation. Semantics. Bayes. Nets. Semantics. Markov Nets. Semantics. Decomposition. Deep Learning Introduction Thanks to Dhruv Batra Georgia Tech http://clgiles.ist.psu.edu/IST597 Class Course outline and materials at https://clgiles.ist.psu.edu/IST597 5 exercises, all in TensorFlow/Python - 40% One large