PPT-CS 678 – Deep Learning

Author : leah | Published Date : 2023-10-25

1 Deep Learning Early Work Why Deep Learning Stacked Auto Encoders Deep Belief Networks Deep Learning Overview Train networks with many layers vs shallow nets with

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CS 678 – Deep Learning: Transcript

1 Deep Learning Early Work Why Deep Learning Stacked Auto Encoders Deep Belief Networks Deep Learning Overview Train networks with many layers vs shallow nets with just a couple of layers Multiple layers work to build an improved feature space. 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). Call 800-678-0141Product Data SheetPDS-224-2 For Precision Fiber OpticAR801 Features:Removable alignment sleeve insert for easycleaning of ber optic terminiThree stages of alignment: shell-to-shell k 1. Boltzmann Machine. Relaxation net with visible and hidden units. Learning algorithm. Avoids local minima (and speeds up learning) by using simulated annealing with stochastic nodes. Node activation: Logistic Function. to Speech . EE 225D - . Audio Signal Processing in Humans and Machines. Oriol Vinyals. UC Berkeley. This is my biased view about deep learning and, more generally, machine learning past and current research!. 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. 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 . 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 . 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”. \n\r\r \n\n \r,\r"#! #\r$\r! # !! \ $*!234'5'+/-U$/'*+,(!./*+-:1+/-!'9!:*=!,-9/*!/-!1/E:*=!/+4,-!+4:*!+4,!(,9';*:+,(!A/00'1':8B!/-!A,31879'&#x 37 ;,B!1/*+-:1+/-9!+4:+!/&#x- -2; 00;'(,!:!9,-&#x- -2; 00;'1,!@,D;DI!97-&#x, 3 ;'9'/* #4$0!'+!+5$!)4$!B$8;-&$5.!'+!68$! .!'+!(+5)!?8';-:$!*+,-! *+,-!(6!+5$!60:!5468$!'+8!H'0)$0)!-0!6&&'8:60&$!=-)4!)4$!*+,- ?3%1'$"#&F+%1%.&*+,- 1!B+&4!&'0)$0)!-5!)4$!5'%$!8$5?'05-�-%-)'9!)4$!$0)-) DIMENSIONS ARE SUBJECT TO MANUFACTURERS TOLERANCE AND CHANGE WITHOUT NOTICEWE CAN ASSUME NO RESPONSIBILITY FOR USE OF SUPERSEDED OR VOID DATADRAWN BYCHECKED BYAPPROVED BYDRAWING NUMBERDATEJAY RMEMBER WB/DPD/E-T2672--7E3227EE/7NOP2A282HN782G2672--7NOP2A3383V3A302HW72-22/BKB-T2-/-01234-567897/11169x -2 2111ABBAAB21A12C-D-EC-D-F/00GH19IBJ6I1AABPJQKJ8MAJ7KL0x -2 M/-/M-NL0MK7/0M/5OP-M7DNL0MO/05C/K8/OLC

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