PPT-Lecture 2
Author : pasty-toler | Published Date : 2015-11-07
MapReduce for large scale similarity computation from last lecture H ow to convert entities into highdimensional numerical vectors H ow to compute similarity between
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Lecture 2: Transcript
MapReduce for large scale similarity computation from last lecture H ow to convert entities into highdimensional numerical vectors H ow to compute similarity between two vectors For example is . Slide . 1. <p> Sample <b>bold</b> display</p>. P. B. #text. #text. nextSibling. prevSibling. nextSibling. prevSibling. firstChild. lastChild. parentNode. parentNode. parentNode. Slide . 1. <. p>Sample . <b>bold</b> display</p>. P. B. #text. #text. nextSibling. prevSibling. nextSibling. prevSibling. firstChild. lastChild. parentNode. parentNode. parentNode. Slide . 1. Google Datacenter. CS 142 Lecture Notes: Datacenters. Slide . 2. Datacenter Organization. Rack:. 50 machines. DRAM: . 800-3200GB . @ 300 . µs. Disk: 100TB @ 10ms. Single server:. 8-24 . cores. Sensors. (bit incomplete. , still). Sensing Categories. Voltage. starting easy: analog in. Distance. acoustic or light. Speed. hard; usu. via distance. Acceleration. accelerometers. Light Level. phototransistors, photodiodes. Assembly Language and . Arduino. Behind the C code (or sketch). C provides a somewhat human-readable interface. but it gets . compiled. into machine instruction set. ultimately just binary (or hex) instructions loaded into the . Intro to IT. . COSC1078 Introduction to Information Technology. . Lecture 5. Audio. James Harland. james.harland@rmit.edu.au. Lecture . 5: Audio. Intro to IT. . Introduction. James Harland. Email:. with. Students. Carl S. Moore, Assistant Director . Carl.moore@temple.edu. Teaching and Learning Center. Temple University . Wood, D., Bruner, J. S., & Ross, G. (1976). The Role of Tutoring in Problem Solving*. Journal of child psychology and psychiatry, 17(2), 89-100.. Slide . 1. Intelligent Systems (AI-2). Computer Science . cpsc422. , Lecture . 11. Oct, 2, . 2015. 422 . big . picture: Where are we?. Query. Planning. Deterministic. Stochastic. Value Iteration. Approx. Inference. More than Just Capturing Lectures. Bill King. What is Lecture Capture?. Lecture Capture is the use of a set of technologies to capture and then enable time-shifted and place-shifted viewing of the Lecture Content.. Lecture 8. Announcements. Lecture 8 > . Announcements. Scores were quite good overall . for homework! . We’re excited! . Destroy the midterm!. Midterm is with CAs. . We will post on the page how to divide into overflow rooms. LCD Text Display. Keypads and Time Slicing. Interrupts. adapted from . T. Murphy’s . lectures. 2×16 LCD . Typically 5×8 dots per character. Note 16 pins: indicator of common interface. Phys 124: Lecture 4. Slide . 1. CSS Rule. body {. font-family: Tahoma, Arial, sans-serif;. color: black;. background: white;. margin: 8px;. }. Selector. Declaration. Block. Attribute Name. Value. CS 142 Lecture Notes: CSS. 1. Intelligent Systems (AI-2). Computer Science . cpsc422. , Lecture . 10. Sep, 29. , 2017. CPSC 422, Lecture 10. 2. Lecture Overview. Finish Reinforcement learning. Exploration vs. Exploitation. On-policy Learning (SARSA). 1. PHY 745 Group Theory. 11-11:50 AM MWF Olin 102. Plan for Lecture 24:. Jahn. -Teller Effect. S. ection 7.7 in . DDJ. Example of tetrahedral molecule with doubly or triply degenerate electronic states..
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