class: title-slide, left, bottom # 20분만에 끝내는 딥러닝 ---- ## **딥러닝 핵심 개념** ### 이광춘 / 한국 R 사용자회 ### 2022-07-01 --- class: inverse, middle name: automation # 발표 개요 ---- .pull-left[ 최근 각광받고 있는 **.warmyellow[딥러닝(Deep Learning)]** 은 학문적으로 의미가 있을 뿐만 아니라 기술의 급격한 발전과 디지털 전환에 중요한 역할을 하고 있기 때문에 **컴퓨팅 사고력(Computational Thinking)**의 중요한 축인 **.warmyellow[자동화(automation)]**을 떼고 생각할 수는 없다. 이에, 자동화, 인공지능, 딥러닝을 이해하고 구현하는데 핵심적인 사항을 다뤄본다. ] .pull-right[ .left[ 1\. [자동화](#automation) 2\. [인공지능](#ai) 3\. **[.warmyellow[딥러닝]](#deep-learning)** ] ] <!--end of right-column--> <!-------------------------- 1. 자동화 -----------------------------------------> --- name: automation-tax # 세무사 업무 변천과정 <br> <br> <br> .center[  ] .footnote[ [이광춘 (2019-12-04), "자동화(Automation) - ~~로봇~~ 자동화가 빼앗는 일자리 그리고 나", 분당 서현 청소년 수련관 ](https://statkclee.github.io/ds-authoring/ds-bundang-2019.html) ] --- name: automation-productivity-gap # 생산성과 임금 격차 <br> <div id="htmlwidget-94bc69cb5913dc8d5da8" style="width:100%;height:67%;" class="plotly html-widget"></div> <script type="application/json" data-for="htmlwidget-94bc69cb5913dc8d5da8">{"x":{"data":[{"x":[1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018],"y":[0,0.0469,0.0112,0.00499999999999999,0.00470000000000001,0.0141,0.0204,0.0232,0.073,0.0704,0.0535999999999999,0.0483,0.0532,0.0347,0.0252,0.00170000000000003,0.0161999999999999,0.0265,0.0525,0.053,0.0608,0.0346,0.0354,0.0544,0.00860000000000005,0.0600000000000001,0.0678000000000001,0.1125,0.1424,0.1323,0.1261,0.1486,0.1872,0.2314,0.2067,0.2602,0.3318,0.3747,0.4103,0.4413,0.4799,0.5019,0.5458,0.5648,0.6428,0.6505,0.6665,0.6883,0.7337,0.7585,0.7694,0.8148,0.8651,0.8811,0.9201,0.9964,1.0873,1.1556,1.1773,1.1833,1.2,1.1945,1.2723,1.2974,1.3307,1.3256,1.3383,1.3334,1.3195,1.3511,1.3728],"text":["Year: 1948<br />value: 0.0000<br />metric: gap","Year: 1949<br />value: 0.0469<br />metric: gap","Year: 1950<br />value: 0.0112<br />metric: gap","Year: 1951<br />value: 0.0050<br />metric: gap","Year: 1952<br />value: 0.0047<br />metric: gap","Year: 1953<br />value: 0.0141<br />metric: gap","Year: 1954<br />value: 0.0204<br />metric: gap","Year: 1955<br />value: 0.0232<br />metric: gap","Year: 1956<br />value: 0.0730<br />metric: gap","Year: 1957<br />value: 0.0704<br />metric: gap","Year: 1958<br />value: 0.0536<br />metric: gap","Year: 1959<br />value: 0.0483<br />metric: gap","Year: 1960<br />value: 0.0532<br />metric: gap","Year: 1961<br />value: 0.0347<br />metric: gap","Year: 1962<br />value: 0.0252<br />metric: gap","Year: 1963<br />value: 0.0017<br />metric: gap","Year: 1964<br />value: 0.0162<br />metric: gap","Year: 1965<br />value: 0.0265<br />metric: gap","Year: 1966<br />value: 0.0525<br />metric: gap","Year: 1967<br />value: 0.0530<br />metric: gap","Year: 1968<br />value: 0.0608<br />metric: gap","Year: 1969<br />value: 0.0346<br />metric: gap","Year: 1970<br />value: 0.0354<br />metric: gap","Year: 1971<br />value: 0.0544<br />metric: gap","Year: 1972<br />value: 0.0086<br />metric: gap","Year: 1973<br />value: 0.0600<br />metric: gap","Year: 1974<br />value: 0.0678<br />metric: gap","Year: 1975<br />value: 0.1125<br />metric: gap","Year: 1976<br />value: 0.1424<br />metric: gap","Year: 1977<br />value: 0.1323<br />metric: gap","Year: 1978<br />value: 0.1261<br />metric: gap","Year: 1979<br />value: 0.1486<br />metric: gap","Year: 1980<br />value: 0.1872<br />metric: gap","Year: 1981<br />value: 0.2314<br />metric: gap","Year: 1982<br />value: 0.2067<br />metric: gap","Year: 1983<br />value: 0.2602<br />metric: gap","Year: 1984<br />value: 0.3318<br />metric: gap","Year: 1985<br />value: 0.3747<br />metric: gap","Year: 1986<br />value: 0.4103<br />metric: gap","Year: 1987<br />value: 0.4413<br />metric: gap","Year: 1988<br />value: 0.4799<br />metric: gap","Year: 1989<br />value: 0.5019<br />metric: gap","Year: 1990<br />value: 0.5458<br />metric: gap","Year: 1991<br />value: 0.5648<br />metric: gap","Year: 1992<br />value: 0.6428<br />metric: gap","Year: 1993<br />value: 0.6505<br />metric: gap","Year: 1994<br />value: 0.6665<br />metric: gap","Year: 1995<br />value: 0.6883<br />metric: gap","Year: 1996<br />value: 0.7337<br />metric: gap","Year: 1997<br />value: 0.7585<br />metric: gap","Year: 1998<br />value: 0.7694<br />metric: gap","Year: 1999<br />value: 0.8148<br />metric: gap","Year: 2000<br />value: 0.8651<br />metric: gap","Year: 2001<br />value: 0.8811<br />metric: gap","Year: 2002<br />value: 0.9201<br />metric: gap","Year: 2003<br />value: 0.9964<br />metric: gap","Year: 2004<br />value: 1.0873<br />metric: gap","Year: 2005<br />value: 1.1556<br />metric: gap","Year: 2006<br />value: 1.1773<br />metric: gap","Year: 2007<br />value: 1.1833<br />metric: gap","Year: 2008<br />value: 1.2000<br />metric: gap","Year: 2009<br />value: 1.1945<br />metric: gap","Year: 2010<br />value: 1.2723<br />metric: gap","Year: 2011<br />value: 1.2974<br />metric: gap","Year: 2012<br />value: 1.3307<br />metric: gap","Year: 2013<br />value: 1.3256<br />metric: gap","Year: 2014<br />value: 1.3383<br />metric: gap","Year: 2015<br />value: 1.3334<br />metric: gap","Year: 2016<br />value: 1.3195<br />metric: gap","Year: 2017<br />value: 1.3511<br />metric: gap","Year: 2018<br />value: 1.3728<br />metric: gap"],"type":"scatter","mode":"lines+markers","line":{"width":1.88976377952756,"color":"rgba(248,118,109,1)","dash":"solid"},"hoveron":"points","name":"gap","legendgroup":"gap","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","marker":{"autocolorscale":false,"color":"rgba(248,118,109,1)","opacity":1,"size":1.13385826771654,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(248,118,109,1)"}},"frame":null},{"x":[1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018],"y":[0,0.0624,0.1046,0.1174,0.1502,0.2082,0.2348,0.287,0.3389,0.3708,0.3808,0.4246,0.4538,0.4784,0.5232,0.5486,0.5832,0.6227,0.647,0.6668,0.7105,0.7439,0.7681,0.8166,0.9134,0.9096,0.8705,0.8686,0.8935,0.9282,0.9566,0.9325,0.8805,0.8736,0.877,0.8849,0.8703,0.8618,0.8725,0.8467,0.8402,0.8393,0.8237,0.8202,0.832,0.8346,0.8389,0.8276,0.8287,0.8487,0.8927,0.9198,0.9296,0.956,0.9949,1.0158,1.0056,0.9973,0.9988,1.0145,1.0139,1.093,1.11,1.0847,1.065,1.084,1.0908,1.1241,1.1439,1.1467,1.1562],"text":["Year: 1948<br />value: 0.0000<br />metric: Hourly compensation","Year: 1949<br />value: 0.0624<br />metric: Hourly compensation","Year: 1950<br />value: 0.1046<br />metric: Hourly compensation","Year: 1951<br />value: 0.1174<br />metric: Hourly compensation","Year: 1952<br />value: 0.1502<br />metric: Hourly compensation","Year: 1953<br />value: 0.2082<br />metric: Hourly compensation","Year: 1954<br />value: 0.2348<br />metric: Hourly compensation","Year: 1955<br />value: 0.2870<br />metric: Hourly compensation","Year: 1956<br />value: 0.3389<br />metric: Hourly compensation","Year: 1957<br />value: 0.3708<br />metric: Hourly compensation","Year: 1958<br />value: 0.3808<br />metric: Hourly compensation","Year: 1959<br />value: 0.4246<br />metric: Hourly compensation","Year: 1960<br />value: 0.4538<br />metric: Hourly compensation","Year: 1961<br />value: 0.4784<br />metric: Hourly compensation","Year: 1962<br />value: 0.5232<br />metric: Hourly compensation","Year: 1963<br />value: 0.5486<br />metric: Hourly compensation","Year: 1964<br />value: 0.5832<br />metric: Hourly compensation","Year: 1965<br />value: 0.6227<br />metric: Hourly compensation","Year: 1966<br />value: 0.6470<br />metric: Hourly compensation","Year: 1967<br />value: 0.6668<br />metric: Hourly compensation","Year: 1968<br />value: 0.7105<br />metric: Hourly compensation","Year: 1969<br />value: 0.7439<br />metric: Hourly compensation","Year: 1970<br />value: 0.7681<br />metric: Hourly compensation","Year: 1971<br />value: 0.8166<br />metric: Hourly compensation","Year: 1972<br />value: 0.9134<br />metric: Hourly compensation","Year: 1973<br />value: 0.9096<br />metric: Hourly compensation","Year: 1974<br />value: 0.8705<br />metric: Hourly compensation","Year: 1975<br />value: 0.8686<br />metric: Hourly compensation","Year: 1976<br />value: 0.8935<br />metric: Hourly compensation","Year: 1977<br />value: 0.9282<br />metric: Hourly compensation","Year: 1978<br />value: 0.9566<br />metric: Hourly compensation","Year: 1979<br />value: 0.9325<br />metric: Hourly compensation","Year: 1980<br />value: 0.8805<br />metric: Hourly compensation","Year: 1981<br />value: 0.8736<br />metric: Hourly compensation","Year: 1982<br />value: 0.8770<br />metric: Hourly compensation","Year: 1983<br />value: 0.8849<br />metric: Hourly compensation","Year: 1984<br />value: 0.8703<br />metric: Hourly compensation","Year: 1985<br />value: 0.8618<br />metric: Hourly compensation","Year: 1986<br />value: 0.8725<br />metric: Hourly compensation","Year: 1987<br />value: 0.8467<br />metric: Hourly compensation","Year: 1988<br />value: 0.8402<br />metric: Hourly compensation","Year: 1989<br />value: 0.8393<br />metric: Hourly compensation","Year: 1990<br />value: 0.8237<br />metric: Hourly compensation","Year: 1991<br />value: 0.8202<br />metric: Hourly compensation","Year: 1992<br />value: 0.8320<br />metric: Hourly compensation","Year: 1993<br />value: 0.8346<br />metric: Hourly compensation","Year: 1994<br />value: 0.8389<br />metric: Hourly compensation","Year: 1995<br />value: 0.8276<br />metric: Hourly compensation","Year: 1996<br />value: 0.8287<br />metric: Hourly compensation","Year: 1997<br />value: 0.8487<br />metric: Hourly compensation","Year: 1998<br />value: 0.8927<br />metric: Hourly compensation","Year: 1999<br />value: 0.9198<br />metric: Hourly compensation","Year: 2000<br />value: 0.9296<br />metric: Hourly compensation","Year: 2001<br />value: 0.9560<br />metric: Hourly compensation","Year: 2002<br />value: 0.9949<br />metric: Hourly compensation","Year: 2003<br />value: 1.0158<br />metric: Hourly compensation","Year: 2004<br />value: 1.0056<br />metric: Hourly compensation","Year: 2005<br />value: 0.9973<br />metric: Hourly compensation","Year: 2006<br />value: 0.9988<br />metric: Hourly compensation","Year: 2007<br />value: 1.0145<br />metric: Hourly compensation","Year: 2008<br />value: 1.0139<br />metric: Hourly compensation","Year: 2009<br />value: 1.0930<br />metric: Hourly compensation","Year: 2010<br />value: 1.1100<br />metric: Hourly compensation","Year: 2011<br />value: 1.0847<br />metric: Hourly compensation","Year: 2012<br />value: 1.0650<br />metric: Hourly compensation","Year: 2013<br />value: 1.0840<br />metric: Hourly compensation","Year: 2014<br />value: 1.0908<br />metric: Hourly compensation","Year: 2015<br />value: 1.1241<br />metric: Hourly compensation","Year: 2016<br />value: 1.1439<br />metric: Hourly compensation","Year: 2017<br />value: 1.1467<br />metric: Hourly compensation","Year: 2018<br />value: 1.1562<br />metric: Hourly compensation"],"type":"scatter","mode":"lines+markers","line":{"width":1.88976377952756,"color":"rgba(0,186,56,1)","dash":"solid"},"hoveron":"points","name":"Hourly compensation","legendgroup":"Hourly compensation","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","marker":{"autocolorscale":false,"color":"rgba(0,186,56,1)","opacity":1,"size":1.13385826771654,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(0,186,56,1)"}},"frame":null},{"x":[1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018],"y":[0,0.0155,0.0934,0.1224,0.1549,0.1941,0.2144,0.2638,0.2659,0.3004,0.3272,0.3763,0.4006,0.4437,0.498,0.5503,0.5994,0.6492,0.6995,0.7198,0.7713,0.7785,0.8035,0.871,0.922,0.9696,0.9383,0.9811,1.0359,1.0605,1.0827,1.0811,1.0677,1.105,1.0837,1.1451,1.2021,1.2365,1.2828,1.288,1.3201,1.3412,1.3695,1.385,1.4748,1.4851,1.5054,1.5159,1.5624,1.6072,1.6621,1.7346,1.7947,1.8371,1.915,2.0122,2.0929,2.1529,2.1761,2.1978,2.2139,2.2875,2.3823,2.3821,2.3957,2.4096,2.4291,2.4575,2.4634,2.4978,2.529],"text":["Year: 1948<br />value: 0.0000<br />metric: Net productivity","Year: 1949<br />value: 0.0155<br />metric: Net productivity","Year: 1950<br />value: 0.0934<br />metric: Net productivity","Year: 1951<br />value: 0.1224<br />metric: Net productivity","Year: 1952<br />value: 0.1549<br />metric: Net productivity","Year: 1953<br />value: 0.1941<br />metric: Net productivity","Year: 1954<br />value: 0.2144<br />metric: Net productivity","Year: 1955<br />value: 0.2638<br />metric: Net productivity","Year: 1956<br />value: 0.2659<br />metric: Net productivity","Year: 1957<br />value: 0.3004<br />metric: Net productivity","Year: 1958<br />value: 0.3272<br />metric: Net productivity","Year: 1959<br />value: 0.3763<br />metric: Net productivity","Year: 1960<br />value: 0.4006<br />metric: Net productivity","Year: 1961<br />value: 0.4437<br />metric: Net productivity","Year: 1962<br />value: 0.4980<br />metric: Net productivity","Year: 1963<br />value: 0.5503<br />metric: Net productivity","Year: 1964<br />value: 0.5994<br />metric: Net productivity","Year: 1965<br />value: 0.6492<br />metric: Net productivity","Year: 1966<br />value: 0.6995<br />metric: Net productivity","Year: 1967<br />value: 0.7198<br />metric: Net productivity","Year: 1968<br />value: 0.7713<br />metric: Net productivity","Year: 1969<br />value: 0.7785<br />metric: Net productivity","Year: 1970<br />value: 0.8035<br />metric: Net productivity","Year: 1971<br />value: 0.8710<br />metric: Net productivity","Year: 1972<br />value: 0.9220<br />metric: Net productivity","Year: 1973<br />value: 0.9696<br />metric: Net productivity","Year: 1974<br />value: 0.9383<br />metric: Net productivity","Year: 1975<br />value: 0.9811<br />metric: Net productivity","Year: 1976<br />value: 1.0359<br />metric: Net productivity","Year: 1977<br />value: 1.0605<br />metric: Net productivity","Year: 1978<br />value: 1.0827<br />metric: Net productivity","Year: 1979<br />value: 1.0811<br />metric: Net productivity","Year: 1980<br />value: 1.0677<br />metric: Net productivity","Year: 1981<br />value: 1.1050<br />metric: Net productivity","Year: 1982<br />value: 1.0837<br />metric: Net productivity","Year: 1983<br />value: 1.1451<br />metric: Net productivity","Year: 1984<br />value: 1.2021<br />metric: Net productivity","Year: 1985<br />value: 1.2365<br />metric: Net productivity","Year: 1986<br />value: 1.2828<br />metric: Net productivity","Year: 1987<br />value: 1.2880<br />metric: Net productivity","Year: 1988<br />value: 1.3201<br />metric: Net productivity","Year: 1989<br />value: 1.3412<br />metric: Net productivity","Year: 1990<br />value: 1.3695<br />metric: Net productivity","Year: 1991<br />value: 1.3850<br />metric: Net productivity","Year: 1992<br />value: 1.4748<br />metric: Net productivity","Year: 1993<br />value: 1.4851<br />metric: Net productivity","Year: 1994<br />value: 1.5054<br />metric: Net productivity","Year: 1995<br />value: 1.5159<br />metric: Net productivity","Year: 1996<br />value: 1.5624<br />metric: Net productivity","Year: 1997<br />value: 1.6072<br />metric: Net productivity","Year: 1998<br />value: 1.6621<br />metric: Net productivity","Year: 1999<br />value: 1.7346<br />metric: Net productivity","Year: 2000<br />value: 1.7947<br />metric: Net productivity","Year: 2001<br />value: 1.8371<br />metric: Net productivity","Year: 2002<br />value: 1.9150<br />metric: Net productivity","Year: 2003<br />value: 2.0122<br />metric: Net productivity","Year: 2004<br />value: 2.0929<br />metric: Net productivity","Year: 2005<br />value: 2.1529<br />metric: Net productivity","Year: 2006<br />value: 2.1761<br />metric: Net productivity","Year: 2007<br />value: 2.1978<br />metric: Net productivity","Year: 2008<br />value: 2.2139<br />metric: Net productivity","Year: 2009<br />value: 2.2875<br />metric: Net productivity","Year: 2010<br />value: 2.3823<br />metric: Net productivity","Year: 2011<br />value: 2.3821<br />metric: Net productivity","Year: 2012<br />value: 2.3957<br />metric: Net productivity","Year: 2013<br />value: 2.4096<br />metric: Net productivity","Year: 2014<br />value: 2.4291<br />metric: Net productivity","Year: 2015<br />value: 2.4575<br />metric: Net productivity","Year: 2016<br />value: 2.4634<br />metric: Net productivity","Year: 2017<br />value: 2.4978<br />metric: Net productivity","Year: 2018<br />value: 2.5290<br />metric: Net productivity"],"type":"scatter","mode":"lines+markers","line":{"width":1.88976377952756,"color":"rgba(97,156,255,1)","dash":"solid"},"hoveron":"points","name":"Net productivity","legendgroup":"Net 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시간당 급여","font":{"color":"rgba(0,0,0,1)","family":"NanumGothic","size":14.6118721461187}}},"hovermode":"closest","barmode":"relative"},"config":{"doubleClick":"reset","modeBarButtonsToAdd":["hoverclosest","hovercompare"],"showSendToCloud":false},"source":"A","attrs":{"53bc3a9b4281":{"x":{},"y":{},"colour":{},"type":"scatter"},"53bc33ec1c7e":{"x":{},"y":{},"colour":{}}},"cur_data":"53bc3a9b4281","visdat":{"53bc3a9b4281":["function (y) ","x"],"53bc33ec1c7e":["function (y) ","x"]},"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script> .footnote[ [Economic Policy Institute, "The Productivity - Pay Gap", July 2019](https://www.epi.org/productivity-pay-gap/) ] --- name: automation-sourcing-strategy # 소싱전략의 변화 <br> <br> .center[  ] .footnote[ 자료출처: Kinetics consulting services / Automation Anywhere ] --- name: automation-apollo # 아폴로 우주선 <br> .center[  ] .footnote[ [Mary (Missy) Cummings (2014), "Man versus Machine or Man + Machine?", Duke University and MIT](https://hal.pratt.duke.edu/sites/hal.pratt.duke.edu/files/u10/IS-29-05-Expert%20Opinion[1]_0.pdf) ] --- name: automation-role-allocation # 인간과 기계 업무분장 <br> .center[ <img src="fig/role-allocation-skill-rules-experties.gif" width = "77%" /> ] .footnote[ [Mary (Missy) Cummings (2017), "Informing Autonomous System Design Through the Lens of Skill-, Rule-, and Knowledge-Based Behaviors", Duke University](https://hal.pratt.duke.edu/sites/hal.pratt.duke.edu/files/u10/IS-29-05-Expert%20Opinion[1]_0.pdf) ] --- name: automation-jobs # Human Supervisory Control <br> <br> .center[  ] .footnote[ [Mary (Missy) Cummings (2017), "Informing Autonomous System Design Through the Lens of Skill-, Rule-, and Knowledge-Based Behaviors", Duke University](https://hal.pratt.duke.edu/sites/hal.pratt.duke.edu/files/u10/IS-29-05-Expert%20Opinion[1]_0.pdf) ] <!-------------------------- 2. 인공지능 -----------------------------------------> --- class: inverse, middle name: ai # 발표 개요 ---- .pull-left[ 최근 각광받고 있는 **.warmyellow[딥러닝(Deep Learning)]** 은 학문적으로 의미가 있을 뿐만 아니라 기술의 급격한 발전과 디지털 전환에 중요한 역할을 하고 있기 때문에 **컴퓨팅 사고력(Computational Thinking)**의 중요한 축인 **.warmyellow[자동화(automation)]**을 떼고 생각할 수는 없다. 이에, 자동화, 인공지능, 딥러닝을 이해하고 구현하는데 핵심적인 사항을 다뤄본다. ] .pull-right[ .left[ 1\. [자동화](#automation) 2\. **[.warmyellow[인공지능]](#ai)** 3\. [딥러닝](#deep-learning) ] ] <!--end of right-column--> --- name: automation-paradox # 모라벡의 역설(Moravec’s paradox) <br> .center[ <img src="fig/moravec-paradox.png" width = "87%" /> ] .footnote[ 미국 카네기 멜론 대학 (CMU) 로봇 공학자 한스 모라벡(Hans Moravec)이 1970년대에 ‘it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility’라는 표현으로 컴퓨터와 인간의 능력 차이를 역설적으로 표현하였다. ] --- name: automation-comparison # 중국어 방 (Chinese room) <br> <br> .pull-left[  ] .pull-right[ | 인공지능 | 컴퓨터 | 인간 | |:---------:|:-----------------:|:---------------------:| | 중국어 방 | 하드웨어 | 인간의 외형적인 몸체 | | 영어만 할 줄 아는 사람| 소프트웨어| 인간의 지능 | | 중국어로 된 질문 | 입력(Input) | 인간이 외부에서 접할 수 있는 자극 | | 중국어로 된 답변 | 출력(Output) | 인간이 외부에서 접한 자극에 대한 반응 | | 질문&대답 목록 | 데이터베이스(Database) | 습득된 기억 | ] .footnote[ [중국어 방 역설 (Chinese room argument) - 대체 누가 중국어를 이해하고 있는가?](http://ko.experiments.wikidok.net/wp-d/592f718da44f1a4153e80611/View) ] --- name: dl-timeline # 기계학습 분류 <br> <br> .center[ <img src="fig/machine-learning-classification.png" alt="기계학습 분류" width="100%" /> ] --- name: dl-timeline # 신경망 모형 여정 <br> <br> .center[ <img src="fig/dl-timeline.png" alt="timeline" width="100%" /> ] .footnote[ Liangqu Long, Xiangming Zeng (2022), "Beginning Deep Learning with TensorFlow: Work with Keras, MNIST Data Sets, and Advanced Neural Networks", Apress ] --- name: dl-applicatin # 딥러닝 활용사례 .panelset[ .panel[.panel-name[이미지 데이터] .center[ <img src="fig/dl-image.png" alt="이미지 데이터" width="100%" /> ] ] .panel[.panel-name[텍스트 데이터] .center[ <img src="fig/dl-text.png" alt="텍스트 데이터" width="100%" /> ] ] .panel[.panel-name[정형 데이터] .center[ <img src="fig/dl-csv.png" alt="정형데이터" width="100%" /> ] ] .panel[.panel-name[...] ] ] --- name: human-performance # 딥러닝 알고리즘 성능 - 이미지 <br> <br> <img src="index_files/figure-html/unnamed-chunk-5-1.png" style="display: block; margin: auto;" /> .footnote[ [Image Classification on ImageNet](https://paperswithcode.com/sota/image-classification-on-imagenet) ] --- name: human-performance-nlp # 딥러닝 알고리즘 성능 - 텍스트 <br> <br> <img src="index_files/figure-html/unnamed-chunk-6-1.png" style="display: block; margin: auto;" /> .footnote[ [SQuAD2.0 - The Stanford Question Answering Dataset](https://rajpurkar.github.io/SQuAD-explorer/) ] <!-------------------------- 3. 딥러닝 -----------------------------------------> --- class: inverse, middle name: deep-learning # 발표 개요 ---- .pull-left[ 최근 각광받고 있는 **.warmyellow[딥러닝(Deep Learning)]** 은 학문적으로 의미가 있을 뿐만 아니라 기술의 급격한 발전과 디지털 전환에 중요한 역할을 하고 있기 때문에 **컴퓨팅 사고력(Computational Thinking)**의 중요한 축인 **.warmyellow[자동화(automation)]**을 떼고 생각할 수는 없다. 이에, 자동화, 인공지능, 딥러닝을 이해하고 구현하는데 핵심적인 사항을 다뤄본다. ] .pull-right[ .left[ 1\. [자동화](#automation) 2\. [인공지능](#ai) 3\. **[.warmyellow[딥러닝]](#deep-learning)** ] ] <!--end of right-column--> --- name: deep-learning-vs-software # 소프트웨어 2.0 vs. 딥러닝 모형 <br> <br> .center[  ] .footnote[ Domingos, Pedro. The master algorithm: How the quest for the ultimate learning machine will remake our world. Basic Books, 2015. ] --- name: hidden-technical-debt # 기계학습(딥러닝) 기여분 <br> <br> .center[  ] .footnote[ Sculley, David, et al. "Hidden technical debt in machine learning systems." Advances in neural information processing systems 28 (2015). ] --- name: deep-learning-end2end # 딥러닝 모형 개발 범위 <br> .center[ <img src = "fig/end2end-deep-learning.png" width = "87%"> ] .footnote[ Eli Stevens, Luca Antiga, and Thomas Viehmann (2020), "Deep Learning with PyTorch", Manning. ] --- name: nlp-transformers # Attention & Transformers <br> <br> .center[ <img src = "fig/transformers-attention.png" width = "100%"> ] .footnote[ [Lilian Weng (June 24, 2018), "Attention? Attention!"](https://lilianweng.github.io/posts/2018-06-24-attention/) ] --- name: nlp-bert-gpt # Attention & Transformers &rarr; GPT, BERT <br> .center[ <img src = "fig/transformers-gpt-bert.png" width = "100%"> ] .footnote[ [ratsgo's NLPBOOK](https://ratsgo.github.io/nlpbook/docs/language_model/) ] --- name: nlp-big-picture # 인공지능 엔진 개발 <br> .center[ <img src = "fig/big-picture.png" width = "100%"> ] <!-------------------------- N. 마무리 -----------------------------------------> --- name: asia-goodbye class: middle, inverse .pull-left[ # **경청해 주셔서 <br>감사합니다.** <br/> ## 한국 R 사용자회 ] .pull-right[ .right[ <img style="border-radius: 100%;" src="fig/korea_R_logo.png" width="300px"/> ] ]