'Current input' presentation slideshows

Latches and Flip-Flops

Latches and Flip-Flops. Discussion D6.1. Latches and Flip-Flops. Latches SR Latch D Latch Flip-Flops D Flip-Flop JK Flip-Flop T Flip-Flop. Sequential Logic. Combinational Logic Output depends only on current input Sequential Logic

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ANTS

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Lab Equipment

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????? ? ????? ???? ??? ????

????? ? ????? ???? ??? ????. Slide from Dr. M. Pomplun. In supervised learning: We train an ANN with a set of vector pairs, so-called exemplars. Each pair (x, y) consists of an input vector x and a corresponding output vector y.

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Pasta Cookers

Pasta Cookers. Service Training. Pasta Station Pasta Cookers. Description Electric. A = Energy regulator (Infinite Switch) B = On / Off Water Control C = Indicator Light D = Basket Control Button E = Overflow Drain F = Water Injectors. Power Connection. Click to View Video.

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Adaptive Resonance Theory (ART) networks perform completely unsupervised learning.

Adaptive Resonance Theory (ART). Adaptive Resonance Theory (ART) networks perform completely unsupervised learning. Their competitive learning algorithm is similar to the first (unsupervised) phase of CPN learning.

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Lecture 7

Lecture 7. Parsing with Context-Free Grammars. Linguistic Representations. Sounds Phonemes, Words, Intonational phrases Words Classes: groups of words which behave similarly Function/Content P.O.S.: Nouns, Verbs, Adjectives, Prepositions Constituents:

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DCSP-18

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Recurrent Networks

Recurrent Networks. Psych 419/719 Feb 22, 2001. Extending Backprop to Recurrent Networks. Consider activation of units at a given time t. Instead of using backprop equations for previous unit activity in space (e.g., previous layer), use previous activity in time. tn. Wba. t2. Waa. Wba.

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Shunt-LDO testing

Shunt-LDO testing. Laura Gonella , M. Barbero , H. Krueger 21/02/2010. Shunt-LDO reminder. Combination of a LDO and a shunt transistor. Shunt-LDO: simplified schematic. 2 Shunt-LDOs in parallel: equivalent circuit. Shunt-LDO can be placed in parallel without problems due to mismatch

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D. Novinsky (PNPI)

PSTP-2011.

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ECE 3110: Introduction to Digital Systems

ECE 3110: Introduction to Digital Systems. Instructor: Dr. Xubin (Ben) He Email: Hexb@tntech.edu Tel: 931-372-3462 Course web: http://www.ece.tntech.edu/hexb/311s04. Lecture 1: Introduction. Digital Design. Principles: continue to be important (correct) for years.

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Introduction to ProLog File Manipulations

Introduction to ProLog File Manipulations. Prof. Dr. Nagi Soliman 2009. File Manipulation

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ECE 2110: Introduction to Digital Systems

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Recurrent Neural Networks

AI: Neural Networks lecture 5 Tony Allen School of Computing & Informatics Nottingham Trent University. Recurrent Neural Networks. The solution to many real world problems require information concerning past inputs.

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ECE 3110: Introduction to Digital Systems

ECE 3110: Introduction to Digital Systems. Instructor: Dr. Xubin (Ben) He Email: Hexb@tntech.edu Tel: 931-372-3462 Course web: http://www.ece.tntech.edu/hexb/311m03. Digital Design. Principles: continue to be important (correct) for years.

202 views • 11 slides

Self-Organizing Maps (SOMs)

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Models of Computation - State Transition Diagrams

Department of Computer and Information Science, School of Science, IUPUI. Models of Computation - State Transition Diagrams. CSCI 230. Dale Roberts, Lecturer Computer Science, IUPUI E-mail: droberts@cs.iupui.edu. Models of Computation. What is a model?

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