data analytics using deep learning
play

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY - PowerPoint PPT Presentation

May 27, 2023 •35 likes •1.67k views

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 0 5 : I N T R O D U C T I O N T O D A T A B A S E S Y S T E M S A N D A D V A N C E D S Q L administrivia Assignment 1 Due on Sep 18

  1. NETWORK DATA MODEL Schema SUPPLIER SU ER PART PA (sno, sname, scity, sstate) (pno, pname, psize) SU SUPPLIES ES SU SUPPLIED ED_BY SUPPLY SU (qty, price) GT 8803 // Fall 2019 39

  2. NETWORK DATA MODEL Schema Complex Queries SU SUPPLIER ER PART PA (sno, sname, scity, sstate) (pno, pname, psize) SU SUPPLIES ES SU SUPPLIED ED_BY SUPPLY SU (qty, price) GT 8803 // Fall 2019 40

  3. NETWORK DATA MODEL Schema Complex Queries SU SUPPLIER ER PA PART (sno, sname, scity, sstate) (pno, pname, psize) Easily Corrupted SU SUPPLIES ES SU SUPPLIED ED_BY SUPPLY SU (qty, price) GT 8803 // Fall 2019 41

  4. NETWORK DATA MODEL • Advantages – Graph structured data models are less restrictive • Limitations – Poorer physical and logical data independence : Cannot freely change physical data storage organization or change logical application schema – Slow loading and recovery: Data is typically stored in one large network. This much larger object had to be bulk-loaded all at once, leading to very long load times. GT 8803 // Fall 2019 42

  5. 1970 s – RELATIONAL MODEL • Ted Codd was a mathematician working at IBM Research. – He saw developers spending their time rewriting IMS and Codasyl programs every time the database’s schema or layout changed. • Relational abstraction to avoid this: – Store database in simple data structures. Codd – Access data via high-level declarative language. – Physical storage left up to implementation. GT 8803 // Fall 2019 43

  6. RELATIONAL DATA MODEL Schema SU SUPPLIER ER PA PART (sno, sname, scity, sstate) (pno, pname, psize) SU SUPPLY (sno, pno, qty, price) GT 8803 // Fall 2019 44

  7. RELATIONAL DATA MODEL Schema SU SUPPLIER ER PA PART (sno, sname, scity, sstate) (pno, pname, psize) SU SUPPLY (sno, pno, qty, price) GT 8803 // Fall 2019 45

  8. RELATIONAL DATA MODEL Schema SU SUPPLIER ER PA PART (sno, sname, scity, sstate) (pno, pname, psize) SU SUPPLY (sno, pno, qty, price) GT 8803 // Fall 2019 46

  9. RELATIONAL DATA MODEL Schema SU SUPPLIER ER PA PART (sno, sname, scity, sstate) (pno, pname, psize) SU SUPPLY (sno, pno, qty, price) GT 8803 // Fall 2019 47

  10. 48 GT 8803 // Fall 2018

  11. RELATIONAL DATA MODEL • Advantages – Set-a-time languages are good, regardless of the data model, since they offer physical data independence – Logical data independence is easier with a simple data model than with a complex one. – Query optimizers can beat all but the best tuple-at-a- time DBMS application programmers GT 8803 // Fall 2019 49

  12. RELATIONAL DATA MODEL • Early implementations of relational DBMS: – System R – IBM Research – INGRES – U.C. Berkeley – Oracle – Larry Ellison Gray Stonebraker Ellison GT 8803 // Fall 2019 50

  13. RELATIONAL DATA MODEL • The relational model wins. – IBM comes out with DB2 in 1983. – “SEQUEL” becomes the standard (SQL). • Many new “enterprise” DBMSs but Oracle wins marketplace. GT 8803 // Fall 2019 51

  14. 1980 s – OBJECT-ORIENTED DATABASES • Avoid “relational-object impedance mismatch” by tightly coupling objects and database. • Few of these original DBMSs from the 1980s still exist today but many of the technologies exist in other forms (JSON, XML) GT 8803 // Fall 2019 52

  15. OBJECT-ORIENTED MODEL Application Code class Student { int id; String name; String email; String phone[]; } GT 8803 // Fall 2019 53

  16. OBJECT-ORIENTED MODEL Application Code Relational Schema class Student { STU STUDENT ENT int id; String name; (id, name, email) String email; String phone[]; } STU STUDENT_ ENT_PHONE NE (sid, phone) GT 8803 // Fall 2019 54

  17. OBJECT-ORIENTED MODEL Application Code Relational Schema class Student { STU STUDENT ENT int id; String name; (id, name, email) String email; String phone[]; } id name email STUDENT_ STU ENT_PHONE NE 1001 M.O.P. ante@up.com (sid, phone) sid phone 1001 444-444-4444 1001 555-555-5555 GT 8803 // Fall 2019 55

  18. OBJECT-ORIENTED MODEL Application Code Relational Schema class Student { STU STUDENT ENT int id; String name; (id, name, email) String email; String phone[]; } id name email STUDENT_ STU ENT_PHONE NE 1001 M.O.P. ante@up.com (sid, phone) sid phone 1001 444-444-4444 1001 555-555-5555 GT 8803 // Fall 2019 56

  19. OBJECT-ORIENTED MODEL Application Code class Student { int id; String name; String email; String phone[]; } GT 8803 // Fall 2019 57

  20. OBJECT-ORIENTED MODEL Application Code Student class Student { int id; { String name; “ id ”: 1001, “ name ”: “M.O.P.”, String email; “ email ”: “ante@up.com”, String phone[]; “ phone ”: [ } “444-444-4444”, “555-555-5555” ] } GT 8803 // Fall 2019 58

  21. OBJECT-ORIENTED MODEL Application Code Complex Queries Student class Student { int id; { String name; “ id ”: 1001, “ name ”: “M.O.P.”, String email; “ email ”: “ante@up.com”, String phone[]; “ phone ”: [ } “444-444-4444”, “555-555-5555” ] } GT 8803 // Fall 2019 59

  22. OBJECT-ORIENTED MODEL Application Code Complex Queries Student class Student { int id; { String name; “ id ”: 1001, “ name ”: “M.O.P.”, String email; “ email ”: “ante@up.com”, String phone[]; “ phone ”: [ } “444-444-4444”, No Standard API “555-555-5555” ] } GT 8803 // Fall 2019 60

  23. 1990 s – BORING DAYS • No major advancements in database systems or application workloads. – Microsoft forks Sybase and creates SQL Server. – MySQL is written as a replacement for mSQL. – Postgres gets SQL support. – SQLite started in early 2000. GT 8803 // Fall 2019 61

  24. 1990 s – BORING DAYS • Multimedia databases – Feature engineering – Accuracy, robustness, and performance SELECT image_date FROM images WHERE event = ‘Sunrise’ GT 8803 // Fall 2019 62

  25. 2000 s – INTERNET BOOM • All the big players were heavyweight and expensive. Open-source databases were missing important features. • Many companies wrote their own custom middleware to scale out database across single-node DBMS instances. GT 8803 // Fall 2019 63

  26. 2000 s – DATA WAREHOUSES • Rise of the special purpose OLAP DBMSs. – Distributed / Shared-Nothing – Relational / SQL – Usually closed-source. • Significant performance benefits from using columnar storage organization GT 8803 // Fall 2019 64

  27. 2000 s – NoSQL SYSTEMS • Focus on high-availability & high-scalability: – Schemaless (i.e., “Schema Last”) – Non-relational data models (document, key/value, etc.) – No ACID transactions – Custom APIs instead of SQL – Usually open-source GT 8803 // Fall 2019 65

  28. 2010 s – NEWSQL SYSTEMS • Provide same performance for OLTP workloads as NoSQL DBMSs without giving up ACID: – Relational / SQL – Distributed – Usually closed-source GT 8803 // Fall 2019 66

  29. 2010 s – HYBRID SYSTEMS • H ybrid T ransactional- A nalytical P rocessing • Execute fast OLTP like a NewSQL system while also executing complex OLAP queries like a data warehouse system. – Distributed / Shared-Nothing – Relational / SQL – Mixed open/closed-source. GT 8803 // Fall 2019 67

  30. 2010 s – CLOUD SYSTEMS • First database-as-a-service (DBaaS) offerings were "containerized" versions of existing DBMSs. • There are new DBMSs that are designed from scratch explicitly for running in a cloud environment. GT 8803 // Fall 2019 68

  31. 2010 s – SPECIALIZED SYSTEMS • Shared-disk DBMSs • Embedded DBMSs • Times Series DBMS • Multi-Model DBMSs • Blockchain DBMSs GT 8803 // Fall 2019 69

  32. 2010 s – SPECIALIZED SYSTEMS • Shared-disk DBMSs • Embedded DBMSs • Times Series DBMS • Multi-Model DBMSs • Blockchain DBMSs GT 8803 // Fall 2019 70

  33. SUMMARY • There are many innovations that come from both industry and academia: – Lots of ideas start in academia but few build complete DBMSs to verify them. – IBM was the vanguard during 1970-1980s but now there is no single trendsetter. – Oracle borrows ideas from anybody. • The relational model has won for operational databases. GT 8803 // Fall 2019 71

  34. GOAL: VIDEO ANALYTICS DBMS • Feature Engineering • Robustness • Computational Efficiency • Usability GT 8803 // Fall 2019 72

  35. CHALLENGES: MULTIMEDIA DBMS s • Feature Engineering – The same multi-media data could mean different things to different people. Second, users typically have diverse information needs. – Thus, a single feature may not be sufficient to completely index a given video. – Therefore, it becomes difficult to identify the features that are most appropriate in any given environment. GT 8803 // Fall 2019 73

  36. CHALLENGES: MULTIMEDIA DBMS s • Robustness – Works well on one dataset, but completely breaks on another dataset from the same domain – Example: Two traffic cameras in different cities – Limits the utility of the database system – Need inherent support for coping with data drift GT 8803 // Fall 2019 74

  37. CHALLENGES: COMPUTER VISION PIPELINES • Computational Efficiency – These pipelines are computationally infeasible at scale – Example: State-of-the-art object detection models run at 3 frames per second (fps) (e.g., Mask R-CNN) – It will take 8 decades of GPU time to process 100 cameras over a month of video GT 8803 // Fall 2019 75

  38. CHALLENGES: COMPUTER VISION PIPELINES • Usability – These techniques require complex, imperative programming across many low-level libraries (e.g., Pytorch and OpenCV) – This is an ad-hoc, tedious process that ignores opportunity for cross-operator optimization – Traditional database systems were successful due to their ease of use (i.e., SQL is declarative) GT 8803 // Fall 2019 76

  39. GOAL: VIDEO ANALYTICS DBMS GT 8803 // Fall 2019 77

  40. ADVANCED SQL 78 GT 8803 // Fall 2018

  41. RELATIONAL LANGUAGES • User only needs to specify what answer that they want, not how to compute it. • The DBMS is responsible for efficient evaluation of the query. – Query optimizer: re-orders operations and generates query plan GT 8803 // Fall 2019 79

  42. SQL HISTORY • Originally “SEQUEL” from IBM’s System R prototype. – Structured English Query Language – Adopted by Oracle in the 1970s. • IBM releases DB2 in 1983. • ANSI Standard in 1986. ISO in 1987 – Structured Query Language GT 8803 // Fall 2019 80

  43. SQL HISTORY • Current standard is SQL:2016 – SQL:2016 → JSON, Polymorphic tables – SQL:2011 → Temporal DBs, Pipelined DML – SQL:2008 → TRUNCATE, Fancy ORDER – SQL:2003 → XML, windows, sequences, auto- generated IDs. – SQL:1999 → Regex, triggers, OO • Most DBMSs at least support SQL-92 GT 8803 // Fall 2019 81

  44. RELATIONAL LANGUAGES • Language – Data Manipulation Language (DML) – Data Definition Language (DDL) – Data Control Language (DCL) – View definition – Integrity & Referential Constraints – Transactions • Important: SQL is based on bags (duplicates) not sets (no duplicates). GT 8803 // Fall 2019 82

  45. ADVANCED SQL • Aggregations + Group By • Output Control + Redirection • Nested Queries • Common Table Expressions • Window Functions GT 8803 // Fall 2019 83

  46. EXAMPLE DATABASE student(sid,name,login,gpa) enrolled(sid,cid,grade) sid name login age gpa sid cid grade 53666 Kanye kayne@cs 39 4.0 53666 15-445 C 53688 Bieber jbieber@cs 22 3.9 53688 15-721 A 53655 Tupac shakur@cs 26 3.5 53688 15-826 B 53655 15-445 B course(cid,name) 53666 15-721 C cid name 15-445 Database Systems 15-721 Advanced Database Systems 15-826 Data Mining 15-823 Advanced Topics in Databases 84 GT 8803 // Fall 2018

  47. AGGREGATES • Functions that return a single value from a bag of tuples: – AVG(col) → Return the average col value. – MIN(col) → Return minimum col value. – MAX(col) → Return maximum col value. – SUM(col) → Return sum of values in col. – COUNT(col) → Return # of values for col. GT 8803 // Fall 2019 85

  48. AGGREGATES • Aggregate functions can only be used in the SELECT output list. • Get # of students with a “@cs” login: SELECT COUNT (login) AS cnt FROM student WHERE login LIKE '%@cs' GT 8803 // Fall 2019 86

  49. AGGREGATES • Aggregate functions can only be used in the SELECT output list. • Get # of students with a “@cs” login: SELECT COUNT (login) AS cnt FROM student WHERE login LIKE '%@cs' GT 8803 // Fall 2019 87

  50. AGGREGATES • Aggregate functions can only be used in the SELECT output list. • Get # of students with a “@cs” login: SELECT COUNT (login) AS cnt FROM student WHERE login LIKE '%@cs' SELECT COUNT (*) AS cnt FROM student WHERE login LIKE '%@cs' GT 8803 // Fall 2019 88

  51. AGGREGATES • Aggregate functions can only be used in the SELECT output list. • Get # of students with a “@cs” login: SELECT COUNT (login) AS cnt FROM student WHERE login LIKE '%@cs' SELECT COUNT (*) AS cnt FROM student WHERE login LIKE '%@cs' SELECT COUNT (1) AS cnt FROM student WHERE login LIKE '%@cs' GT 8803 // Fall 2019 89

  52. MULTIPLE AGGREGATES • Get the number of students and their average GPA that have a “@cs” login. SELECT AVG (gpa), COUNT (sid) FROM student WHERE login LIKE '%@cs' GT 8803 // Fall 2019 90

  53. MULTIPLE AGGREGATES • Get the number of students and their average GPA that have a “@cs” login. AVG(gpa) COUNT(sid) SELECT AVG (gpa), COUNT (sid) 3.25 12 FROM student WHERE login LIKE '%@cs' GT 8803 // Fall 2019 91

  54. DISTINCT AGGREGATES • COUNT , SUM , AVG support DISTINCT • Get the number of unique students that have an “@cs” login. SELECT COUNT ( DISTINCT login) FROM student WHERE login LIKE '%@cs' GT 8803 // Fall 2019 92

  55. DISTINCT AGGREGATES • COUNT , SUM , AVG support DISTINCT • Get the number of unique students that have an “@cs” login. COUNT(DISTINCT login) 10 SELECT COUNT ( DISTINCT login) FROM student WHERE login LIKE '%@cs' GT 8803 // Fall 2019 93

  56. AGGREGATES • Output of other columns outside of an aggregate is undefined. • Get the average GPA of students enrolled in each course. SELECT AVG (s.gpa), e.cid FROM enrolled AS e, student AS s WHERE e.sid = s.sid GT 8803 // Fall 2019 94

  57. AGGREGATES • Output of other columns outside of an aggregate is undefined. • Get the average GPA of students enrolled in each course. AVG(s.gpa) e.cid SELECT AVG (s.gpa), e.cid 3.5 ??? FROM enrolled AS e, student AS s WHERE e.sid = s.sid GT 8803 // Fall 2019 95

  58. GROUP BY • Project tuples into subsets and calculate aggregates against SELECT AVG (s.gpa), e.cid FROM enrolled AS e, student AS s each subset. WHERE e.sid = s.sid GROUP BY e.cid GT 8803 // Fall 2019 96

  59. GROUP BY • Project tuples into subsets and calculate aggregates against SELECT AVG (s.gpa), e.cid FROM enrolled AS e, student AS s each subset. WHERE e.sid = s.sid GROUP BY e.cid e.sid s.sid s.gpa e.cid 53435 53435 2.25 15-721 53439 53439 2.70 15-721 56023 56023 2.75 15-826 59439 59439 3.90 15-826 53961 53961 3.50 15-826 58345 58345 1.89 15-445 GT 8803 // Fall 2019 97

  60. GROUP BY • Project tuples into subsets and calculate aggregates against SELECT AVG (s.gpa), e.cid FROM enrolled AS e, student AS s each subset. WHERE e.sid = s.sid GROUP BY e.cid e.sid s.sid s.gpa e.cid 53435 53435 2.25 15-721 53439 53439 2.70 15-721 56023 56023 2.75 15-826 59439 59439 3.90 15-826 53961 53961 3.50 15-826 58345 58345 1.89 15-445 GT 8803 // Fall 2019 98

  61. GROUP BY • Project tuples into subsets and calculate aggregates against SELECT AVG (s.gpa), e.cid FROM enrolled AS e, student AS s each subset. WHERE e.sid = s.sid GROUP BY e.cid e.sid s.sid s.gpa e.cid 53435 53435 2.25 15-721 AVG(s.gpa) e.cid 53439 53439 2.70 15-721 2.46 15-721 56023 56023 2.75 15-826 3.39 15-826 59439 59439 3.90 15-826 1.89 15-445 53961 53961 3.50 15-826 58345 58345 1.89 15-445 GT 8803 // Fall 2019 99

  62. GROUP BY • Project tuples into subsets and calculate aggregates against SELECT AVG (s.gpa), e.cid FROM enrolled AS e, student AS s each subset. WHERE e.sid = s.sid GROUP BY e.cid e.sid s.sid s.gpa e.cid 53435 53435 2.25 15-721 AVG(s.gpa) e.cid 53439 53439 2.70 15-721 2.46 15-721 56023 56023 2.75 15-826 3.39 15-826 59439 59439 3.90 15-826 1.89 15-445 53961 53961 3.50 15-826 58345 58345 1.89 15-445 GT 8803 // Fall 2019 100

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

DATA ANALYTICS USING DEEP LEARNING GT 8803 FALL 2018 POOJA BHANDARY T O W A R D S A U N I F

DATA ANALYTICS USING DEEP LEARNING GT 8803 FALL 2018 POOJA BHANDARY T O W A R D S A U N I F

DATA ANALYTICS USING DEEP LEARNING GT 8803 FALL 2018 POOJA BHANDARY T O W A R D S A U N I F I E D A R C H I T E C T U R E F O R I N - R D B M S A N A L Y T I C S TODAYs PAPER SIGMOD 2012 In-RDBMS Analytics Hazy project at

317 views • 31 slides
DATA ANALYTICS USING DEEP LEARNING A U T O M A T I C D A T A B A S E M A N A G E M E N T S Y S

DATA ANALYTICS USING DEEP LEARNING A U T O M A T I C D A T A B A S E M A N A G E M E N T S Y S

DATA ANALYTICS USING DEEP LEARNING A U T O M A T I C D A T A B A S E M A N A G E M E N T S Y S T E M T U N I N G T H R O U G H L A R G E - S C A L E M A C H I N E L E A R N I N G S I D D H A R T H B I S W A L TODAYs PAPER

496 views • 32 slides
DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 0 8 : Q

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 0 8 : Q

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 0 8 : Q U E R Y E X E C U T I O N administrivia Reminders Sign up for discussion slots on Thursday Proposal presentations on next Wednesday

1.32k views • 112 slides
DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 0 9 : Q

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 0 9 : Q

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 0 9 : Q U E R Y O P T I M I Z A T I O N administrivia Reminders Assignment 1: postponed to next Monday Sign up for discussion slots on Thursday

1.68k views • 150 slides
DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 0 6 : D

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 0 6 : D

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 0 6 : D I S K - C E N T R I C A N D I N - M E M O R Y D A T A B A S E S Y S T E M S administrivia Project ideas List shared on Piazza Start

1.22k views • 80 slides
DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 1 2 : T

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 1 2 : T

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 1 2 : T R A I N I N G N E U R A L N E T W O R K S ( P T 1 ) administrivia Reminders Integration with Eva Code reviews Each team must send

934 views • 70 slides
DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 0 7 : S

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 0 7 : S

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 0 7 : S T O R A G E M O D E L S & C O M P R E S S I O N administrivia Reminder Assignment 1 due on next Wednesday Sign up for discussion

1.09k views • 63 slides
DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 0 1 : C

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 0 1 : C

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 0 1 : C O U R S E I N T R O D U C T I O N WELCOME TO 8803-DDL This is a cross-cutting course! Gain holistic understanding of three areas Data

1k views • 67 slides
DATA ANALYTICS USING DEEP LEARNING GT 8803 // SIDDHARTH BISWAL L E C T U R E # 0 3 : B L A Z E

DATA ANALYTICS USING DEEP LEARNING GT 8803 // SIDDHARTH BISWAL L E C T U R E # 0 3 : B L A Z E

DATA ANALYTICS USING DEEP LEARNING GT 8803 // SIDDHARTH BISWAL L E C T U R E # 0 3 : B L A Z E I T : F A S T E X P L O R A T O R Y V I D E O Q U E R I E S U S I N G N E U R A L N E T W O R K S TODAYs PAPER BlazeIt: Fast

280 views • 23 slides
Effectiveness of Deep Learning Vs. Machine Learning in a Health Care Use Case RxToDx A Data

Effectiveness of Deep Learning Vs. Machine Learning in a Health Care Use Case RxToDx A Data

Effectiveness of Deep Learning Vs. Machine Learning in a Health Care Use Case RxToDx A Data Science Machine Learning/Deep Learning Show Case Dima Rekesh/Julie Zhu/Ravi Rajagopalan Optum Technology November, 2017 Analytics Machine

483 views • 32 slides
DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ S P E A K I N G T I P S

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ S P E A K I N G T I P S

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ S P E A K I N G T I P S CREDITS Based on a talk given by: Margaret Martonosi (Princeton) Computer architect GT 8803 // Fall 2019 2 MOTIVATION

871 views • 75 slides
UNIFIED MEMORY FOR DATA ANALYTICS AND DEEP LEARNING Nikolay Sakharnykh, Chirayu Garg, and Dmitri

UNIFIED MEMORY FOR DATA ANALYTICS AND DEEP LEARNING Nikolay Sakharnykh, Chirayu Garg, and Dmitri

UNIFIED MEMORY FOR DATA ANALYTICS AND DEEP LEARNING Nikolay Sakharnykh, Chirayu Garg, and Dmitri Vainbrand, Thu Mar 19, 3:00 PM RAPIDS CUDA-accelerated Data Science Libraries PYTHON DL RAPIDS FRAMEWORKS DASK / SPARK cuGraph cuDF cuML

720 views • 46 slides
DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2018 // JACOB LOGAS L E C T U R E # 1 0 : L

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2018 // JACOB LOGAS L E C T U R E # 1 0 : L

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2018 // JACOB LOGAS L E C T U R E # 1 0 : L O C A L I T Y - S E N S I T I V E H A S H I N G F O R E A R T H Q U A K E D E T E C T I O N TODAYS PAPER Locality-Sensitive Hashing for

458 views • 41 slides
DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 2 0 : A

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 2 0 : A

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2019 // JOY ARULRAJ L E C T U R E # 2 0 : A D V E R S A R I A L T R A I N I N G administrivia Reminders Best project prize Quiz cancelled Guest lecture GT 8803 // Fall 2019

1.1k views • 47 slides
DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2018 // JENNIFER MA L E C T U R E # 1 3 : F

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2018 // JENNIFER MA L E C T U R E # 1 3 : F

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2018 // JENNIFER MA L E C T U R E # 1 3 : F O C U S : Q U E R Y I N G L A R G E V I D E O D A T A S E T S W I T H L O W L A T E N C Y A N D L O W C O S T TODAYS PAPER Focus:

1.06k views • 56 slides
DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2018 // JENNIFER MA L E C T U R E # 1 4 : L I

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2018 // JENNIFER MA L E C T U R E # 1 4 : L I

DATA ANALYTICS USING DEEP LEARNING GT 8803 // FALL 2018 // JENNIFER MA L E C T U R E # 1 4 : L I V E V I D E O A N A L Y T I C S A T S C A L E W I T H A P P R O X I M A T I O N A N D D E L A Y - T O L E R A N C E TODAYS PAPER Live

709 views • 56 slides
Attacks on DNS: Risks of Caching March 21, 2018 The Inside Story of How Facebook Responded to

Attacks on DNS: Risks of Caching March 21, 2018 The Inside Story of How Facebook Responded to

Attacks on DNS: Risks of Caching March 21, 2018 The Inside Story of How Facebook Responded to Tunisian Hacks It was on Christmas Day that Facebook's Chief Security Officer Joe Sullivan first noticed strange things goingon inTunisia. Reports

670 views • 46 slides
Ponnurangam Kumaraguru Computation, Organizations and Society Carnegie Mellon University Feb 23

Ponnurangam Kumaraguru Computation, Organizations and Society Carnegie Mellon University Feb 23

Trust and Semantic Attacks - II Ponnurangam Kumaraguru Computation, Organizations and Society Carnegie Mellon University Feb 23 rd 2006 ponguru@cs.cmu.edu http://www.cs.cmu.edu/~ponguru/ CMU Usable Privacy and Security Laboratory Outline

548 views • 38 slides
JavaScript: Memory, Passing, and Objects CS 115 Computing for the Socio-Techno Web Brian Brubach

JavaScript: Memory, Passing, and Objects CS 115 Computing for the Socio-Techno Web Brian Brubach

JavaScript: Memory, Passing, and Objects CS 115 Computing for the Socio-Techno Web Brian Brubach JavaScript Primitive Data Types string number boolean true or false null undefined symbol JavaScript

333 views • 22 slides
Internet Internet 1962: Paul Baran, packet switching 1969: DARPA, 4-node ARPANET 1974:

Internet Internet 1962: Paul Baran, packet switching 1969: DARPA, 4-node ARPANET 1974:

History Internet Internet 1962: Paul Baran, packet switching 1969: DARPA, 4-node ARPANET 1974: Vint Cerf, Bob Kahn, TCP 1982: TCP/IP ARPANET 1984: DNS 1986: NSFNET Backbone Network, 56kbps 1992 : Tim Berners-Lee,

250 views • 9 slides
Computer Systems Dr. Barry Wittman Not Dr. Barry Whitman Education: PhD and MS in

Computer Systems Dr. Barry Wittman Not Dr. Barry Whitman Education: PhD and MS in

Computer Systems Dr. Barry Wittman Not Dr. Barry Whitman Education: PhD and MS in Computer Science, Purdue University BS in Computer Science, Morehouse College Hobbies: Reading, writing Enjoying ethnic cuisine

767 views • 44 slides
Logist ics Cour se Web Page 1: Welcome and Overview Mailing List St af f Text book

Logist ics Cour se Web Page 1: Welcome and Overview Mailing List St af f Text book

Logist ics Cour se Web Page 1: Welcome and Overview Mailing List St af f Text book Last Modif ied: 6/ 1/ 2004 11:53 AM -1 -2 Topics What is an operat ing syst em? OS Hist ory, Archit ect ural Support A sof t ware

573 views • 8 slides
Chapter 10: Case Studies So what happens in a real operating system? Operating systems in the real

Chapter 10: Case Studies So what happens in a real operating system? Operating systems in the real

Chapter 10: Case Studies So what happens in a real operating system? Operating systems in the real world Studied mechanisms used by operating systems Processes & scheduling Memory management File systems Security How are

408 views • 22 slides
Using OpenBSD Security Features to Find Software Bugs Peter Valchev pvalchev@openbsd.org

Using OpenBSD Security Features to Find Software Bugs Peter Valchev pvalchev@openbsd.org

Reflections|Projections 2007 Using OpenBSD Security Features to Find Software Bugs Peter Valchev pvalchev@openbsd.org Overview of OpenBSD History Berkeley Software Distribution (BSD) First freely distributable BSD was released in June

810 views • 24 slides

More recommend