{"id":215,"date":"2021-10-12T07:33:35","date_gmt":"2021-10-12T07:33:35","guid":{"rendered":"https:\/\/drfarshadmohammadian.ir\/?p=215"},"modified":"2024-05-09T10:39:09","modified_gmt":"2024-05-09T10:39:09","slug":"cursos-de-excel-vba-python-e-mais","status":"publish","type":"post","link":"https:\/\/drfarshadmohammadian.ir\/index.php\/2021\/10\/12\/cursos-de-excel-vba-python-e-mais\/","title":{"rendered":"Cursos de Excel, VBA, Python e mais!"},"content":{"rendered":"<p>Tecnologias de c\u00f3digo aberto s\u00e3o amplamente utilizadas em conjuntos de ferramentas de ci\u00eancia de dados. Quando hospedadas na nuvem, as equipes n\u00e3o precisam instalar, configurar, manter ou atualizar localmente. Um desafio cr\u00edtico \u00e9 a necessidade de profissionais qualificados que possam preencher a lacuna entre a Engenharia Mec\u00e2nica e a Ci\u00eancia de Dados\/IA. Os Engenheiros Mec\u00e2nicos precisam <a href=\"https:\/\/www.florestanoticias.com\/2024\/05\/07\/como-a-ciencia-de-dados-e-o-aprendizado-de-maquina-estao-revolucionando-o-mundo-dos-negocios\/\">curso de cientista de dados<\/a> adquirir conhecimentos e habilidades em an\u00e1lise de dados, aprendizado de m\u00e1quina e programa\u00e7\u00e3o para aproveitar efetivamente essas tecnologias em seu trabalho. As universidades deveriam adaptar os seus curr\u00edculos para dotar os engenheiros das compet\u00eancias necess\u00e1rias. Essas plataformas tamb\u00e9m oferecem suporte a cientistas de dados experientes, disponibilizando uma interface mais t\u00e9cnica.<\/p>\n<h2>Ci\u00eancia de Dados Impressionador Hashtag Treinamentos<\/h2>\n<p>A Ci\u00eancia de Dados se tornou um dos campos mais promissores nos \u00faltimos dois anos. Isso porque organiza\u00e7\u00f5es orientadas a dados conseguem se mostrar n\u00e3o s\u00f3 mais eficientes como mais competitivas e inovadoras. Uma aplica\u00e7\u00e3o proeminente da IA na Engenharia Mec\u00e2nica \u00e9 o design generativo. Ao aproveitar algoritmos de IA Generativa, os engenheiros podem explorar uma ampla gama de possibilidades de design e gerar solu\u00e7\u00f5es ideais com base em restri\u00e7\u00f5es e objetivos espec\u00edficos.<\/p>\n<div style='text-align:center'><iframe width='568' height='314' src='https:\/\/www.youtube.com\/embed\/BmytbT9cim8' frameborder='0' alt='ci\u00eancia de dados impressionador' allowfullscreen><\/iframe><\/div>\n<h2>Ci\u00eancia de Dados e Intelig\u00eancia Artificial na Engenharia Mec\u00e2nica<\/h2>\n<p>E o y ser\u00e1 a coluna survived para que possamos fazer uma compara\u00e7\u00e3o com os modelos de classifica\u00e7\u00e3o. Dependendo do visual, outras bibliotecas j\u00e1 podem ter op\u00e7\u00f5es mais prontas para usarmos nas nossas an\u00e1lises, que \u00e9 o caso do pairplot no Seaborn. Esse \u00e9 um gr\u00e1fico de bloxplot que \u00e9 muito interessante para an\u00e1lise de dados, se voc\u00ea ainda n\u00e3o sabe como ele funciona pode acessar nossa aula de Estat\u00edtisca para Data Science.<\/p>\n<ul>\n<li>Al\u00e9m de ter conhecimentos em programa\u00e7\u00e3o, ele precisa saber criar modelos estat\u00edsticos e ter o conhecimento e dom\u00ednio apropriado de neg\u00f3cios.<\/li>\n<li>Aqui estamos verificando o mesmo gr\u00e1fico, mas apenas com as tarifas menores do que 100 reais.<\/li>\n<li>Auxilia na cria\u00e7\u00e3o de conte\u00fados de variados temas voltados para aqueles que acompanham nossos canais.<\/li>\n<li>Compilamos a m\u00e9dia de avalia\u00e7\u00e3o e de n\u00famero de coment\u00e1rios do Class Central e de outros sites de avalia\u00e7\u00e3o para calcular a m\u00e9dia ponderada da nota de cada curso.<\/li>\n<li>Infelizmente, n\u00e3o existem avalia\u00e7\u00f5es dispon\u00edveis nos principais sites de avalia\u00e7\u00e3o que utilizamos nesse guia.<\/li>\n<\/ul>\n<h2>Conhe\u00e7a todos os Cursos da Hashtag Treinamentos<\/h2>\n<p><img decoding=\"async\" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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9ejgR5JGCIilndiFVFHElieAFAXGW6CtxHZ6KprjWFXs\/RXIXWn4VD7ySDQ4N5jKC\/uM7BPnw2y8WXOcF2Xl8HjWiukHW\/rdyjwz6jLJHNnbRN3brxOcK0CKyqMcg1Yba5GSifRLV+sKzt8+MXMMGOYllgjP0O4Nau63OvDSpdK1G1t7+Gaee1lgSOJxIzNMpiIUpkE4Zjz4YrgGUjJYjLk5LcySeJJPfzrwv28+7uFQ22iJU1KLT5o2Aurxk42wD6cj6zwqqSYHlx+utcpJ6Mn6RVdpuouhAU4Uc1PwT9Hwa0OmUNboCNv+OWff\/szomvDVqstWVsAkBj2Z\/QauanNa2ihr4apRezNWPaUxTFQaBShoKEilMUxQg6Z8DBc3eo9n8mh5f81q6f3fpNcweBg2LvUf6tD+taun96K6sP6h6voP9JHx+rG69Jrzd+k17vRTeCtxbnm79JqCCPgeJ5muS+kXhV6jBdXNstlbMttczwqxN1tMsErxKzYkxtEIDVvXwttSHKwtfpu\/aVr3kTkeOop2v5HZG79Jpu\/Sa4499xqXxC1+m69pT33GpfELX6br2lN5Ejr9HXyOx936TTd+k1xx77jUviFr9N17SnvuNS+IWv03XtKbyI6\/R18jsNI\/Kbieypm79JrjceFtqXPxC14+m79pXvvuNS+IWv03XtKbyI6\/R18mdj7v0mm79Jrjj33GpfELX6br2lPfcal8QtfpuvaU3kR1+jr5HY+79JqWY\/KHE8q4899xqXxC1+m69pXnvt9SznxC1z8t37Sm8iOv0dfI7I3fpNN36TXHHvuNS+IWv03XtKe+41L4ha\/Tde0pvIjr9HXyOx936TTd+k1xx77jUviFr9N17SnvuNS+IWv03XtKbyI6\/R18jsOWPiOJ51M3fpNcbnwttS+IWvD03ftK999xqXxC1+m69pTeRHX6Ovkdj7v0mm79Jrjj33GpfELX6br2lPfcal8QtfpuvaU3kR1+jr5HY+79JqXPHw5nsrjz33GpfELX6br2leN4W+pH\/uFr9N37Sm8iOv0dfI7HEfpNe7v0muOPfcal8QtfpuvaU99xqXxC1+m69pTeRHX6Ovkdj7v0mm79Jrjj33GpfELX6br2lPfcal8QtfpuvaU3kR1+jr5HYrx8DxPKvIY+A4nlXHZ8LfUviFr9N17SvF8LfUhw8Qtfpu\/aU3kR1+jr5M7I3fpNN36TXHHvuNS+IWv03XtKe+41L4ha\/Tde0pvIjr9HXyOx936TTd+k1xx77jUviFr9N17SnvuNS+IWv03XtKbyI6\/R18jsOCPgeJ5mpm79JrjdfC21IcrC1+m79pXvvuNS+IWv03XtKbyI6\/R18jsfd+k03fpNcce+41L4ha\/Tde0p77jUviFr9N17Sm8iOv0dfI7H3fpNS0j4txPZXHnvuNS+IWv03XtK6K6h+nEus6XFqlzGsE00k6NHDtmMCCZ4VIMhLZIQE\/LWSmm7I20sTTqO0WZ7u\/Sa83fpNe70VBLOACayN5h\/Wd0qg0y0mvbuQxwW65c8SSWIREQD4Ts7KoHea+f3W71q32uzsjMbexRmMNmpOwE5CS4YcJZtkZ7hnA7z0V4cHS1Y7GDTwcy31wrMh5+L23lucdxkMS\/PXHdxdBFZFHlFWB78kKnH5hJ85rTNtuxsglxJKAkcDhVByxxgD0CoFiBBI59hPb8lUxZmVVXiB9GT+mq+y0qY8kJJPcTw7tnsGaxdkbYxcuCKCWLPBfhdvcBUsrs4HZ6eXdWYWXRG7l4iFioHMqVUDHE7R+b6arYeru7yDIgXHIEgj5ax6xBc0blg6suEWYRuMYJGc8sYwf2CoiMHOOPaOI+vjWfw9XVyxJ8nlkcQM8uOz3fRUvVurq4jG15Lkcxkhv0EHu5\/NWPWIamfZ9b2WYTDIpPLHYDyIP8ArtFZboV1tLst8JeB9I7DxrGtQ0mSPO2pQjv5VO0O6IZSTwPBh2fKO7j\/AI1LtJZFL0pg3VpOLWazRmdKgSva1HhbHjA\/NXhWoqUJueqOFeN6KUoQdNeBd\/teo\/1aH9a1dQ1y74GAPjeo44fyaH9a1dPbLd9dWH9Q9X0H+kj4\/VkylS9lu+vQp763FufMXp1\/SOof1+9\/vMtWarz06\/pHUP6\/e\/3mWrNXIeVqes\/iKUq4dHNFnvrmGxsozcXVy4jhiXGWbBYkliAiKqsxYkBVUk4ANDFK+SLfSuj7XwULjYRbnV7e3vZFytoIpJVLeakzTxu4zkZEXZyrFOh3g+3l3qd\/o1xcxWdxpkcMryKDeRTx3JYRNHsujR5C5w4DDI4dtTsvQ6Xg6qtkabpWZ9U3QF9a1L7Exzravu5337o0q4t8AjYVlPHPfwrL+g3UNNqGp6tpC3qQvojxpJOYpHW43zSKCiCUGPG65EnnUWZhDDzlZpc7GnaV0Dr\/AILV8kEk+nX8GqyQgk2yBoJHwM7ETmR0Mp7FcoD3isX6oeoy61m2k1KW5j0nTo3eMXNwrO8jxHZlKwl4wsSNlCzuvlBhg4NTsvQyeEqp2sampW0+uTqcbRLa3v01GDU7W6m3EbQq0cpk2GlLJGrypJEFQ5YOCCyDB2uGU9E\/BgvZrZLvVL6HRd8FMcEqGeZQ4yqz5miSGUj7wMxHbg5AbLIWFqOWzY0HStk9cvU1f6BsTXBW7sZm2Ir6AOEEmNoRzxNxgdgGI4spwfKzwrE+r7o22p6haaYkgge9l3SzMpkWM7LPtFAQW4IeGRUW5GuVKUZbLWZYqV0lN4J0wYxLrFuZ\/vYWhdGJxkAgXBYAjt2TWqtd6qL+y1ez0K\/CwS6hPDDbXabU1pKk8qwb+JsK0gRnG0hCsOGQAykzstcUbJ4WpDijAqV0fd+Cs0TbE2uW0T4B2HiKNg8jstdA44GtHdYXRz7G6hc6aJ1vPFGRfGohsxTbyGObKDabGN7snieKmjTXEiph501eSLDSt1dVnUBJq+mR6x9kYrGGR5kKSxM+xuJmg2ml3yr5TLw4doqV1g9R8WlxW9xPrVvLDcXkNrI8cZY2yTrITdOi3DF4k3flAY4HOeGC2XxMuqVNnatl4GmqVtvrD6hNQ0+70+zt2XU01dhFaXUKvHELjBd45ss+7QQgzbeSCiSEDyDVv6zOqOXTdQtNEtbpdY1S9GfFLeNoWg2sbkSPJKyguolc52dhI9psKQaWZjLDVI3uuH8\/U1pSujrTwULjYjW61a3tryVcraCN5gW81JmnjaTjwyI\/prE+ivg\/3lzq15oVzcxWdxYW6XJmUG7hnilcJGUCujx5DZw4DDHLBBLZehm8HVVsjTtK6D1PwZxFHLKddtWMKO5TdgMxjUsU\/wBq8knGOVYH7k8n2tjpZ4yu5LBfEd2+98q+GnZ3+82fhHbxs8uFGmuREsLUXFcr8uRrilK2j1MdS9zrsU14LmLTrC2cxyXM33V96qq7BYFdcIFdcu7oOPDawcQlc006cpu0TV1K211ydRlzolrHqSXUep6e7Kj3MKmFoml\/mmaIyOrQsfJDq54kDAyKyjot4LV5PbQz39\/FpdzdDahsXjaebiu2scrb5NibHEogfZ7+Bqdl8DcsJVctmxz7Str6D1CanPq8+hSGK2ls40nnunYtA1rMSsM9sgAkuQ5VxjChSjBipABvvT\/wbrmysJ9Tsb+HV4rNZJLqOJDBIkUILTtEVmlWV0UFihKnAOMnALZZCwtVq9jRdK6C6N+DKbuK3li1q327mGOYQCIySJvIxKUIW5yxUE5OBy5CtYdcnQJtCv8A7GPcpeuIIpnljUxBDMZAIXjLsVcLGr8TykU440aa4ieGqQjtSWRhlKUqDnFd4eBn\/wDDdr\/z73+9y1wfXd3gag\/a3a4OPu97\/e5azp+t4Fj0b\/1H8DdFSrv4J+Q17st31LuVbZPHsroLw4B8OLbGuxtIcQjT4d12jO+uN4FHYxIXPzVpfozorXk2D5KsdpvkznH8fkrevh8HGqWI5k2bkk8iN+wUD5PK9asE6lY0ZJGx90BxnuTGeH\/37K48TNwi2ju6PoqrVUZcDLujvQ2BAgMYJyD28TggEkDOe3GRitj6L0fhUAxxLkYySMg\/LnJzj9NWPTfOwAoyMt8H6O\/Of9cKzLQ7mPmzNLw4LGpA9OAcDHlHjVE6kpPNns40IRWSQa32RsnZHH4IA4fICPSeFQNp7P8ABQHOOOyB6eePlq8XM6nBSGUfKIuJ7OcnE8h81UFxcyt5PGGM\/CLCJWI7vI2mxjuIrTJNM6IRViivNGMa5kMMeePlMikdo5GsM1zBJG9iY\/8AAxHbw5sQefOs3lt02W3cZkbtnk8hAe9S2S3H0fPWJ61Aucffd3ADj8vPurbyMXFczXXSrSg6PkA5HMcQe3hs\/BP8OVanxsOV7ifRW+7xFJ2ew8CeB4H71sVpzpdppguXiYY2vKQ9hXntKccRgGrPBTycWeb6XpLKaL1YvtIrd6g\/VU3NUehnMKegY+gkVWbuuhnyPER2Kso6N\/UizwpXoFKg5yGQ8Khiz83ZUygqMyb5HTXgXnF3qOfi0P61q6f3g765g8C8fyvUf6tD+taun9gd1deH9Q9T0H+kj4\/VjeDvptjvpsDupsjurcW58xOnX9I6h\/X73+8y1ZqvHTr+kdQ\/r95\/eZas9ch5Wp6z+IrdngU3UMfSJVnIDzWN1Fa7WONztQS7KZ+\/MEVz82a0nUy1uHjdJYnaKWJleOWNmjkjkQ7SPG6EMjggEEHIIqU7Myo1N3NS0NuddnQTW5ekV4\/ilzdzXN40mn3UMc0kXixkzYiK6QbEG6j3anLLuyhJI+EdleB1YXVnretWWrB4tTa3illWdjJPPsztvbjelj4whM0R3oLA7YOTmtd2HhM9II4RAZoZmC4FzLApuOWAxMbrEzekofTmtfR9PdSGofZrxyQ6pnPjZKliMbO7MZXdbjZ4brZ2MdlTdJ3Wp2KtShNTi5PPgbt8FnoTqFp0nuHurSaCG0ivUkuJY5YoGZ5FSIRSuoSbb+ENgnKgnlWwPB2ukl6T9MJIztIbmBQw5ExTXkL47xtI3HtrR+t+Epr9xA1tvorbbXZa4tod1c4PBtmR5GWNiM+UiqR2EGsP6r+su\/0N7iTTGjDXixrOZk3+RCZGQrlhg5lfPPOaJpW+JnDE0oOKV7Xbv8Tqjqi6H6T0dtdU6RaZfSa5CsDicWptpkAtT4xJGiW3BpxtDi7eSpJ4Ak1r3qq6xZTpE1vregzat0fvb26kSaziW8SOSa5N9NBNbuVykVzIWSbKcRgZZOGm+rPrP1HRGuDp0i7N4F8YhmTfQs6E7Moj2hsyYZlyOYODnAxeOgnXprGlRm3spIvE9uR4rKWFGtrYzSNM6WwjKyRRbbthNsqOwVO0THGQytdccrXNwdNerDRbSDSOlOnLLptjHqmmS3djd+MIhtpr6GKWRob4mW3kTAY8SrRq2OGywofDc6LaldX9ncwQS32mi1EUS28ct0sF2ZZGnLxQqxRpENvhyMMExnycVpnrO61tT1wJHqUym3ibbjtIEENsJcFd6VyXkk2WIBdmxk4xk5vfQbr+1vTYFs4Z0ubeIBYUvIzcNCg4COOVXSQoBwCszADAGAAKhtcDGWIoyvDNJ2zWq7jcXSCzmsOrw2mugpdPu47S3mOZ4tu+WaxgIPFXjhTb2OaIhXhs4GivBx\/+I9I\/rf8A\/VLVp6xusPUdalWXVLgzCLO4gULFbQbXwt1CnDaOOLttMeWcACrT0S16bT7uDULTZFzaPvIS42029kr5SZG0MMe2jeaNNSvF1ItcFbyN1+FN0P1G76TSyadZXFwzxWYhnghnKCVIlwVu1URxFWx5RcbJHEitr9dsmxcdCLS9dZdXTVbBp3GCzCMQRXsmfvUe5aE+nZ\/4TWkpvCe6QEFRJAhPJlt12l9I23K5+UGtb3HTW+k1GLWric3Wo280M8c0+HAe2cSxJulwiQBh\/NoFHE4xmp2lyN0sTTi243d2vDM6769m0X7Igar0cvtcuvFof5dZW8txbiHbm2LcyJdxjeIdsldngJF48a446Ywql7drFbSafCbiVreyuEeG4tbaVzJbQyxyEsrCF4+ZPfk5ydr++g1\/z7b83Pta1b056T3GqXk2p3xU3Vzu96Y13cf3GJIE2UydnyIk7eeaiTuzDGV6dT1ddDq3qP8AFvtG\/wC0LOXVbPe3O+0+1Rprq4H2ROysUayIWKvsOfKHBD8laT69ItMaC3k0bQL3QTHK3jVzfQT28MqOoEUSu1xKu3tAnB2T3Z41QdX\/AF5atpFnHptg0ItomkdBLFvZNqZ2lfL7wZG057KrukHhCaxeLCl14tKlvcRXUaG3BQz25Lws6tIdoK5DAd6qeyjaaSM5YilKmot8kuB1N1BR3Gm6Npen67cJHf3RdNNtpcCeKERNPDZHJzLNFAjkgY2VIj47ILaW6ltOuNP6c3EGvPvdQuYbw290\/krdyzmOWG4g2uCo1tFcIEB8jZaMfBxWmOnvWXqOq3dvqN7Ns3NiE8TMA3Mds8cgmEsSZOzMZFRi3M7CdigCo6x+tTUNZa2lvzGs9gxa2ubaM21yjEqw+7I+0MOiuMY2WGRipcuHcTLGU8rX9Fq3eZZ19dBtal6RXknilxdvc3W3p1zDHNLF4ttDxNIrhF2IN0uypBZdgqScA7R2H4IGj3tpr+r2+rJJHqAs1a4M7GWWVnuEO+M5ZvGFbmJAzA99a907wmNfihEBmhnYLsi4mgVrjlgEmN1jZvSyHPbmsZ6M9cWrWd9c6stwLm+vYxFPNdIJhulYOqRxqVWFQRwVQFAJwKi6vcxjWoxqKacuNzanWVDockOoxWXRTUotScXKwX5tbg263ZL7N0XW8bMO35edg8Dy7KullYy3HVosNtE9zM8oKQwJJPK4XXttiscSlmAUFjgcACeysCl8J7X2BUvb4YEH+T8cEY4fdedWPoB16avpNlDplg0ItbbeboSw7yQb2R5n2n3g2vLkbspdXM3iKW1e\/FNcNbGB6t0fu7ZVe8tJ7RHbZR7iC4tkd8E7KtMihmwCcDsFZX1MdXN9rtw9nZloLI7v7J3R2xbRxI28jWRFIFzPtLlIj98MnZALCLrO63tS1yCK11Nomit5t\/GIYty292HiyzbZyNmR+Hpp1Y9b2paHBLa6a0QhuJt\/IJot8293aRZVtsYGzEnD0VjlzOOO6VTi9k3l1061FptvpfRawtLg6Npd5p76tqk8NwLPc291FO0XjRiEUjtKTJI4wgOEXOWCT\/Cq6N6jda\/oE9jDLPCu5WGaFJJI7W6S8WWWWSRAVtxu9w22xAIiPmmtM9N+vrWNUs5tNvWhNrdBBKI4d3IRHIkqhX3h2fKjXs5Zqb0R8ITXLC1SxinjnhhURwPcxb+eGNRhESUOu2qjlvA+MAcgBWbkmdksVSleLbtl4W5G0vCz0a51HpDpen6MrNqZ0+QTNGzxbm1mmdA9zOn81bBfGNrPMSYwxdVNx6W6Z9qnR6bo\/pVvcapqerRStfXkNvcyW0HjEQt57hniRkhCwpsRxZLeTtt3toHop1uarY311q8c63F\/foI7qa5QTbahlZQqKVWMLu1UKuFCjAAAGMsuvCa150eNnt9mRWRsQYOHBU4O954NNpXbCxVJuUs035IyfwYJ30GbN\/od7NdawbVbLULe2E6RWNxhvLkLgRRlmSRwvlYQbQygFYp4XXQuLTNY3lvK8q6pE15IkztPNFMZXjkG9cl2hbZBXaJIwwzgABovhLa\/BEsBmhutkbKzXECtPgDAy0DxqxHDiVJPbmtZdMOk11qdy9\/qMxubqXAMjbKhUXOxFFGgCRRLk4VQBxJ4kkmG1axpq1qe52I3f8FopSlYnAK7v8DRh9rdr\/z73+9y1whXd\/gaKPtbtf8An3v97lrOn63gWPRv\/UfwNy7wd9QyuMHjUWwO6hQd1dBeHEf\/AOIPYYm0q6HaLqAj5DDKv\/1fTWoepPhO6LxEigkc8cD\/AANdZ+G50bW40Oa4CbUunyw3MZGAVXbEFwT3ruZpDj\/hB7K5t6hrRDJdNy3UEYGf\/nEszZPdusY9NcWM9Rlh0a3vkbKtYl2dqVgqg82IGBzPPh2GrxZ9K7SMFXnjjUDhtuibXHZznPaQfr9Na7h6P3muTBbdvFtPiL7qR8jfEHZ3oRTkjtGccCO\/NZMOpPT4U\/lV07SnPlqVGQeY3eCw5f6HCqyNCCzm\/A9M8VUllCOWryM\/6NXcd2GNvcLMEwSsTxzAA8OLBjsn5xV0TSsuM+Vn5zxwPkrW3QfoxDp0++srve4Oy6OoU7JBwHO18LOOwD\/Hao1MtG0gHFBzHd8Innnv4VrnThfJnVTqTteSsa\/63ek66bGIIo\/GLyYfc0JIjRSSoeTHFgCpOyOPDmOdalh6Ga1qo30su4iLZAY7qIDh\/NRIu0\/Z8Inlz79ndJtTge5NyyCSdEKAnDdoYKAeGQcnPIcSe2rd0x6RXlg8MN2rxmaIzySJFLNb2MAQzsLgxyRDfpCN60SvK4TDbKlgo66Cdv8Ajj4nDjHFP\/lm7ckjX2qdX+p6bmaKUXCcNtcEg8fNx8nLB4mrb1k23jFjb32zsSW7hJRkkqshMbqeXASBOfea27dalc2ty9jeMJ1IyksIZkwRkEiQbURwR5JZvl7KtXTDQQ9tcxJj+VQuUwPJ3uztRn0+UE+isXVcZra4pmieHjKk9i9muD8jR\/R5\/IKnmGP0HiKumavPRbo5FJpIulGZptuYP98gjYxbs9myWUnh5w7qsCx+mupTUm7cmfM+nujJ4WqqkuE1tL+V+ak+lQpwFe7VSefPaV5tU2qA6a8DDPjeo4+LQ\/rWrp7Ld1cxeBd\/teo\/1aH9a1dQ11Yf1D1XQf6SPj9WS8t3VJvGbYb5KqqkagfubfIa3FufMXpif5dfZ+OXX94kq1V3zL4O3R6Zmnls3aWdmllbxnUFDSSkySMFW4AUFmJwOAqH3tfRz4k\/51qX7xXPu5dxSy6Nm23dHBFK7397X0c+JP8AnWpfvFPe19HPiT\/nWpfvFN3LuI7Mqar88Dgild7+9r6OfEn\/ADrUv3inva+jnxJ\/zrUv3im7l3DsypqvzwOCKV3v72vo58Sf861L94p72vo58Sf861L94pu5dw7Mqar88Dgild7+9r6OfEn\/ADrUv3inva+jnxJ\/zrUv3im7l3DsypqvzwOCKV3v72vo58Sf861L94p72vo58Sf861L94pu5dw7Mqar88Dgild7+9r6OfEn\/ADrUv3inva+jnxJ\/zrUv3im7l3DsypqvzwOCKV3v72vo58Sf861L94p72vo58Sf861L94pu5dw7Mqar88Dgild7+9r6OfEn\/ADrUv3inva+jnxJ\/zrUv3im7l3DsypqvzwOCKV3v72vo58Sf861L94p72vo58Sf861L94pu5dw7Mqar88Dgild7+9r6OfEn\/ADrUv3inva+jnxJ\/zrUv3im7l3DsypqvzwOCKV3v72vo58Sf861L94p72vo58Sf861L94pu5dw7Mqar88Dgild7+9r6OfEn\/ADrUv3inva+jnxJ\/zrUv3im7l3DsypqvzwOCKV3v72vo58Sf861L94p72vo58Sf861L94pu5dw7Mqar88Dgild7+9r6OfEn\/ADrUv3inva+jnxJ\/zrUv3im7l3DsypqvzwOCKV3v72vo58Sf861L94p72vo58Sf861L94pu5dw7Mqar88Dgild7+9r6OfEn\/ADrUv3inva+jnxJ\/zrUv3im7l3DsypqvzwOCKV3v72vo58Sf861L94p72vo58Sf861L94pu5dw7Mqar88Dgiu7PAzJ+1y2x+HvP71LVX72vo58Sf861L94rPegXRS10q3+x2nRmG0iZnSNnlmIaZjLId5MzOcuzHBPDsrKEGndnVhMHKjLabXAv2W7qEt3VMoa3Fiac8JdXm0i+tEAMt3CYIlyF2nkxjGeZABOO3FcldRVq8dzf2kymOUW8YZG4MuGwMgnzZQfnrtfrW07eQM3bERIvypz+omuZ9R0prfW4LsqVj1XT5E2iDstcWjxMcNyyYU5f8JNVeNqO7jytdeBddH4eLgqqealZruayMsjYWVhspwkWPEYQeWxXhhcr8M8TxGMmsP6w7eddNguLSd5byWSZp47dPGUtmVV8XtVtHZN6kh29u8nWVgygBVD7K7JihVgobiNnBHPuz+kfQKqoI7dFIOzsnBIZQfSMHHGuCliFB3aLypht5HZu\/AweNraa2sJnhMV4EcXRiUWq4DssDTxo5jiuHiWJ3WDbUMzLk4GL614Y45YIcqrKMnOdkjgeJGfqqrulVyd2hkPYcbKgj5vJXPfxq12gZmZAu2eOSMkd55dnA1qrVtt3R24fDqmlHTUxNCVbAH3USBwwEeSVIYcXRl4FQdkjBPPPDGQa50Yi1WSK61FRPPBGsccjiDOyhyokWOJUlPFsbatjPAgVbdZs3Vi7L5PaF+Fw7QDVx0CZZNkLKQTgbLZU57v0fT6ayVWSWTMp4eEnmiqu9FVWZ+Ls2SSxdyWJJLGRyWbiT9Jqg1FCFAPYfJ\/ZWZwaWcceOe3tPo41YekcGw2znu+burC7ebMJ00lZGE2thBb6ebaPIljS7wv3skbSSyMMc1ZSVI7wnZ26hQcBW0dcQqLm6LYSFJIY4wM7TzxKS7EnyVDNjhnPGtYtyFWGGbzZ83\/zapFuhBcoy87L+Bs02air2uq54O5Bs02ajpS4udL+BgubvUc\/Fof1rV0\/uhXMHgYHF3qPb\/Jof1rV09vPQa6cP6h6voP8ASR8fqz3dCqfUIhu3\/JNT956DUjUJPub8D8E1uLcn2nwE\/JX9AqZUu0+An5K\/oFTKAUpUE74Vm57IJx34GaAjpXKaeE1qBAPilsM8cfyk4z6d9xqJfCZ1DttLcj\/+SPr3pxWnrECn7dwntP5M6qpWh+g3hH208iwapB9jy5Ci6RzPbAngDMCqvCmfvvLA5kgZNb2jcFQykMCMgjBBB4ggjmCK2RmpcDvw2LpYiO1Tlf8ANCKlcq3PhMagpbFpb4UtgHxknAzgEiXn81dTwtlQe8A\/SM1EKkZcDDC46lib7t3txytxI6UpWZ2Clad6+et+TRp7ezs4o7i4kjaa4E28xFGW2IAojYZZisxOTwCjzqoepLrsl1W+On30UVu0kTPatDvRvJYvKkiYSu2WMe04xj+bbvFa97Ha2TgfSVBVty5ele3j8TeFKVrrr76fTaLaQXVtEk7z3IgKzbzYVTFLKWAjYHazEBz7TWcpJK7OqtWjSg5y4I2LStG9TXXt9kbvxDUo47SWfAs5IjIIpZeObeTesdmRhxU5wSCvMrneVRGakroww2Kp4iG3Td0KVzh0+6\/72y1C8sYraB4rSd4Ud\/GC7LGcbTFZAMnieArevQPWGvdPsr6RRHJd20MzouSiNKgdlXa47IJ4ZqI1FJ2Rrw+OpV5yhB5rjl4F6pXLep+EnfxySxraW5WOSRVz4znCMVGSJcZwB2V0jd6kVs3vAoLLbNOEydkssRl2M88ZGM0jUjLgMNj6OI2t2+HHIudK5f0Hwjr+a4t4HtLcJPPDG5XxkMFlkVGKkykBsMcZBrqCkKilwJwmNpYlN03e3gKUrQnXF13XelalNp1vbwyxQpCweXfmRjLGsrZ3cigAbWOXZUzmoq7MsViqeHht1HlexvulYd1N9LJNW02HUbhFhlleZGSLb3YEMrxKRtktxCjtq7dPtZay0+8vo1EklpbSzIj52GaJCyq2yc7OQOVSpK1zZGtGVPeLha\/hxL3SubugnhA315f2dlLawLHd3EULunjAdVlYIWQtIRkZzxFdI1jCalwNWFxlLExcqbuk7aClYz1n9Kl0rT7nUGAZoUxBGcgS3EhCQRnHHZLsCcclDHsrn+08Jm92031pAYQ673d+MCTdbQ3m72pcbzZzjPDOKiVWMXZmvFdJUMPJRqOzZ1NSpFjdJLGk0TB4pUWSNxxV45AGRh6CpBqfWw7k7ilKUAqnSMF3z\/w\/oqoqnV8O\/DPwf0UBN3QpuhXm89BpvPQaAs\/SawV42UjIYEEd4Iwa5s1vWFluJ7SdcPp7bdqCPwKupEZ8\/dCUbI4kM3YTXUt4NpcbJrU\/THok5maVYzIjHazwLRtghtkc+IJOefE1X9IUnOKa5Fx0RiIwlKErZrJvVGK6cDIoVTxPp4Y7CKr49AxxJ5dp+Tj+isa6HXBQGCQbEls7QsDwZQjYjJDcc7GwPmrMdR1VVhZ2ONlWJx2BQST6OC1StLgz1FGo9lNGNaorO7wx5S1iXZlkXm7OePLsC5z+V6KqehN3ZAbKkuRtLGzK8QJjJRiokUErwYZGeHHjWGal0mkk\/k1om27eW75IVNoggMRjH3p58qk6d0VvXKzhhEXUbTu+Cv3zeQ5xlthB6Bj053U6PNirWTyjd\/AvHWd0piiOzFEuQo2nO0EwxIUtsgljnhwH0DFYto\/SK3dg84CMccFyAhAPnAEttA\/VV3u+jER2Te3AiC\/eRGNdsngSQjcsZGT29\/ba7zorAqYt7W6uUD7DOkecvgMNkMoDYXZPAYxg9uTuUItd5G3UjySXebF6PdIYpV2Y3DgcMjtIAJGPO4g49IPaKs3SyXyie\/8ARjgOHKte9HSgEV3aTN4i5UhnBiKnayAeHlEvnPLOePfWcdL\/ALwDiSoYZwOeMcDjFc1WOy7IRqbSd1mag6f6i6yNar5MbbMknaWPEIpPYoCg47zWKE1fesBybyTP3qxj\/wBoPZ6Sax93xVpRSUEfF+nasqmOqXd7SaXwRHmma8U0rYU57mma8FKA6c8C7\/a9R\/q0P61q6hrl3wMBm71Hs\/k0P61q6f3fpNdOH9Q9V0H+kj4\/VkdSNQ\/m3\/JNTN36TVPqEf3N+J+Ca3FuT7X4Cfkr+gVMqVafAT8lf0CptAKlXnwH\/Ib9BqbUq8+A\/wCQ36DRkPgfOayXO7B5EoD8hwDXac3UToRGPEivcVuL8EekZuMfSK4rtWwEYcxsn6MGt3TeEtqZBC21qp7CUumx83jI41wUpQV9o8J0ViMLS2+sJPha6vrf4GD9dXQ5NI1KSxgcywGOOaAvgyLHNtDdyFQAzKyOM4GRiulPBa1d7jQ4llJY2cs1qjHOTFHsSRKCfvVSZUHcEA7K5S1bULzV74yybV5f3jhVSNcs2BspFFGnBY1VeXYASTzNdn9TXRE6VpdvYyYNxhpborgr4xMdt1Uj4SoNlAe3Yz21nQzm2uB3dCx2sVUqU01Cz82rI4Vvf958r\/419GbX4C\/kr+gV85r3\/efK\/wDjX0ZtfgL+Sv6BU4bi\/wA1Nn+N8avh\/wCxMqRqF0kMck8rCOKFGklc8kjjUu7H0BQTU+tKeFr0u8V09NNibE+psRJjgVs4SGl4jltuY0x2qZO6umctlXPRYrEKhSlUfJf\/ADzOcelOqza1qklwqlptRuVjtojzRXZYLWHhkDZTdqSOGcntqVdRXOjamV+DeaXdAqeIV2hYMjd5hlTBweaycedXnqQ12ysNSTUNT2ylrG5t0iTek3LjdqzAsAFVGkP5Wyeyq7r+6T2Gp3seoaZvA8kIjvFlQRZeEhYZVIY7bGM7B7hEnfVfbLavnc8FsRlReIc1t7d7Xztrb4+R2L0X1iO9tYL63OYbqJJU7wHGSjdzqcqR2FTWnfDO\/o2y\/wDEB\/dbmrf4HfS3bhuNFlbyrcm5swfwEpAuI19Cysr\/APrt3VcPDO\/o2y\/8QH91ua65S2qVz1WIxKxHR0qmsc\/jzOVkYggglSCCCCQQRxBBHEEHtFdgeDr1ojVIPEr1gNUtU4k4HjsC4AuFH4UZAcDtIYcGwug+o3oRFrMl\/ZSHdyrZ720n4nc3CzRqrMB8KJgzKy9zHGCARisqXek32Dm01DT5sgjiUkXiCDykidG\/JdH7Q1c0JOHpcmecwFergtmtxhLJ+H880XPrn\/prVP67N\/1Guw+pn+hNK\/qFr+qWuI+luste3dxfOoje7laV0UkqrPxYKTx2c5xmu3Opn+hNK\/qFr+qWtmHd5ss+gpqeKqyXB5\/NnDGv\/wA\/cf8AOm\/62rvLUv6Jl\/8ADn\/uxrg3X\/5+4\/503\/W1d5al\/RMv\/hz\/AN2NTh+Y6A9at4f+xwr0P\/22y\/rVt+uSvoZXzz6H\/wC22X9atv1yV9DKnC8zZ\/jPqVPiv5FcYeFH\/T93\/wAu1\/u8ddn1xh4Uf9P3f\/Ltf7vHWeI9XxOn\/Iv0q\/8AJfRm4PBu6Y6da6Jb293f29rOstyWhnntoJVD3EjKWjkkDAEEEcOINX\/ra6daZNpGpQwajazTS2VwkUUdzaSSSO0bBUjRJCzuTyAGa5l6K9Vmq6hbreWNrvraQuqSb20iy0bGNxsSyqwwykcqqdb6ntZtYJbu5tBHb20bSzSb6yfYjjG07bKTlmwAeABNalUns2tlYrqfSOKWHUFRdtm21Z8LceBbep7+mtL\/AK9bfrFrvOuDOp7+mtL\/AK9bfrFruHpVrUdja3F9OcRWsTyv3tsDIRe92bCgd7Cs8N6rOr\/HJKNCbftfwjm3wwOl2+uoNHibMdkBPdAcjdTL9xRh3pC21\/6\/orUOrdF54LKz1KVcW+otOsJwcg2zBDt55bflle8ITVFq+pPeXMt3dPiW7maWeQAsEMr7TlEJyVUHAXPJQK3d1l9Yuh3uijR7UTI9okP2PLxAKktquwgZt4SNuMyIW\/8AmE1obU2238CmqShjJ1as5JZeim9OHkvmzOfBL6W+Nac2nStmfS2Cpnm1nLloD\/5GEkeByVU763RXC\/Uj0t+xeq21052baQ+L3nd4tOQC59EbiOT\/ANM99d0A11UJ3j8D03QmL3+HSfGOT\/jy+gpSlbi4FSovhv8A+X9FSr27CcTVLp94JGcq3DyeWMcqCxdKVBu\/Sabv0mgI6lXMIYEYqLd+k15u\/SaA0d1q6LuboXkYxvU3c2OTMnGJiPOxkZ9ArGb8GW3nUqWJiyEzs5AyHUZzsn08fnrdvTfQxcRtGT8JeB7mHI+jjWkrWVopWR+EsRKyKRjI8oMFzwwRg\/R6ao+kKOxUVRcH9T03RGJ2qbpvivoYjO8sEQuIOElzLbw3N1jbNpGxSKedLcfCZEBfZ5tgZJJrcek6Ho8cEQkuo7h2hf8AlBmy9yfIk3uxG\/leRKvADGGHDlWGxwxqSvw48cMkMhUkrsceZwAO34I+U2y00cRM72jtGshzIscj28p7MMy4L49J9FY05xvmWE6DmkoycTccUGnRlza2e8DoAXjg2dkDO06z3AReWDlW+9HbWtusvrGn3c0FmkdtM7KBMjC8kiYDdyyHCC33gUR7IzIAynaGABVBqizMSs1y5hbP3OeeefO1zDAkK\/ZzHGrPqFzCxXdfdyqgKQNmFQOAIYA8OZxx4Ha9Nb5VVbJEU+j6cc6krvS5arDTUgtUtSNnaxtKdph5WCNo4JLnCja55zVdr96JZNvO3sRRoSchtoAFydrBPMnHD5+FWzULjizTNgnBVfJ4BeJIAweAx83ZVtOohgVU5bOMZ+C7MVUlCeA4Zx3Lw7a5ZJtGdSolJmtekV1vbmaTHAvsj5IwEH\/Tn56tzpmp9z5Lup5q7qflDEf4VCDVhHJHw7FVJTrTnLi5NvxZAopUea9qTmuSlr2oxXtBc6X8DBsXeo5+LQ\/rWrp7fCuYvAu\/2vUf6tD+tauoa6sP6h6voP8ASR8fqyXvhUjUJRu3\/JNVdU+ofzb\/AJJrcW5MtPgJ+Sv6BUypdr8BPyV\/QKmUAqVefAf8hv0GptQuuQQeRGD8hoQ+B85LJcmMHkSgPyHANdsjqS0L4gP7W9x9G\/qni6idCUgiyPkkEfyi\/I4HI4GfiK2YK56VHZ9axRdGdEbja3yjK9rc9dUWPox0QsbDP2PtYrUsMO8aKJXUcg8x+6OPQSavZq3Xmv2kTmKa6hhkXG1HJLDG65AYbSOwIyCD8hqstbpJFEkTrLGeToyuhxzwynBrercEXUVCK2Y2+CPnVdDJcd5b9JrekfhM3oAHiUHAAfCn7OHfWw36suihJYmHJJJ\/l84GScnA8b4CrhadSXR2VBLDbb2Js7MiXV68Z2SQcOtyQcEEfNXJGlOPBr88Dy2H6LxtFvdVIK\/HP+rNf9FvCLu7m8tLSSzhRLq6t4HcPNtIs8qRM67RxkB88e6tU9dnS37Kapc3attW0Z3Fn3eLQFgrr6JHMkv\/AKnoro3Suqzow8wS2WOW4jbaEUd7cySqyHOTGl0WBBH1VU23U70cSdIlgXxqMrItu11dPJ5GHBa3e4JdMDJBUgjnwqXTqSVm1+eBurYDG4insVKkGtrjfy4I110Z8Gsz2tvPc3rW080SSS24hV9w0ih90WMw2mXIB4cwaqdR8GMLFI0GoNJMqOYY2hRFkkCkxozic7ClgBnBxmuhZdShWVbd5kW4kGY4GeNZnXjxSInaYeQ3ED7091U0\/SOzRmjku4UkQlXRpoFdWHAqys+Vb0Gtu5gWHY+CSs4r43f+zhXq+6RPpeoW1+oINtL93i4qzwNmO5hKn74xs4APJgD2V0J4YFykuk6fNEwkilvUkjdeKvHJaXDI6ntBBB+esyvupvQ7uSS8e13r3UjzPJHPdrHJJKxd3VYpwgBYk4UY41O1ro7oVzb22h3MkTw2LKLWz8bZJ4njRoVXyZxM5CuwwxP1CtapSUXHI4qHRlalQqUXKNperm+Py5rQ0t4Gn9JXn9RP94hrafhC9V41a38btFA1S0Q7rkvjcIyxtXY8NvJJRjyYkcAxIyboh1faXo7y3VjD4ozx7E0sk1xIoiDByCbiVlQbQU54chWRaVrdtc5FrcRXJX4QhkimK\/lbtjitkKdo7MjtwvR6jherVrPjw+OVj55yxlSUcFXUlWVgVZWUlWVlPFWBBBB5Gu7+pn+hNK\/qFr+qWrL046s9Bmlk1DVIkhkmI3sz3E9nG74A2iEmSPeEAZOMnmc1mHRLxRbaKDTXSS0to0hh3MguERIlCom822LEADiSTWNGk4SZo6K6MlhKsnKSaay1480cBa\/\/AD9x\/wA6b\/rau8tS\/omX\/wAOf+7GsN1vqc6Ox7U13AsAmdsvLd3kCNI+XYKWuQM\/COB2D0VsPUjbx25S4ZIrQx7ljI4ij3bruwm8ZhjKnA45pSpuN7mXRnR88M6m2456ePHJanz50m7MMsM6jaaCSOVVPJjEyuAcdhK1vL3zd78Sg9af9tbKl6nejKbrbiVPGMeL7V5drvwdnZ3Obn7rnbT4OfhDvqPUepjo3AAbiBYA5wplu7yIM3PZUvcjJx2CtcaVSPBr88Cvw\/ReOw6ap1ILX82TE+rfr8utQ1G00+W0ijjupCjSI0xdAEdwVDHBOVHOtaeFH\/T93\/y7X+7x10RoHVfoVjewS20SxahFmW3ja5uXl4q6mRbeWc7a7O3xKkcD3VV9NurDR72aTUdSg2pSi76cz3VugSJdlS4SZY1AUAZwOVZunOUbN8zrr4DFV8M6dScXLavflZL4fwWjwVv6Atv+dd\/3mSsk66P6E1X+oXP6pqufQfRrOztI7bSwBZAu0WxI9wrGR2d2EzuxfLs3accuypl94pqEVzYs6XUUiNDdwxyAsqSbUbI5hfbiJww7DkHurco2hbuLWnRccOqTavsW8bWOJOp7+mtL\/r1t+sWt2+GJ0t2YrfRYm8qci6vAPwMbFbaJvQ0oZ\/8A0V76zvSeqnQbO7t5IYVivo3E1qj3N00pePJV0gluDvQCpPIjyT3VP6b9XGh3d143qiL43d7KqZLm4tzMYlSFFiiWdVJA3Ywg5nvPHRGlJQayKaj0ZXpYWdFSjeT43drWz5cTnXqR6pW1xLmd5zZwW7pEjiMTGWZl3kiYLrshEaI9ud4O6tje9fj\/ABk35un7xW6ej2j2WkWgt7cLZWUTM5Mkh2Q0jbTM81w5JJJx5TcgAOAFXLStWguVL2s0dyi8GaGSOZQe4mNiAazjQilZ8Tqw3QuGhTUakU5Wzd3\/ALRw51t9CH0a+awd9\/GY45YJyu730UgKklNohSsiSpjJ+CDwziupfBw6XfZHSYhI21dWGLW4ycswiUbiY54nbiKZY82V6unWd0W0e83EmubtTCHW3eWd7LhJsl1DJKm8GUU4OcccYyan9W\/QvS9PEs+jKFS7CCWRJ5rqOQQlzHsmSV1GN6\/Ed9RCm4zy4GGC6OnhsVKUHHYf\/bd37vk+\/gZhSleMa6C+Mb6UTAFQeRYA\/OQB+mrP0buN3dNHyV12hzxlSM\/U31VXdIH2pFXGRkkj5B+3FW2ybF7CPOEg5HHkqeZ5dlcVWpatFfmZa4WCeGnfmn5W+5nomFN8KjTkK9rtKol74U3wqZXjHFAUd2Qc\/kmueuumBra9S6jBMNwgSZRw2JI3GzKewqVZVPdgHvzvvUtSVDj0VojrM6XW13deIwMJZrbbM5XBjjLFUMJYHjMDglRyxx48K5MbbdO53dHX36sYtZapxIZgrZOzhc7RzyVC3EZymTzOScYqel5GWxKMNydSS3ByQSu0OK5GOGfhd9Y\/q+nyJlocbJba2fLwF5MCw4bR2uA4fL3WK414xuY7jIYszKMMrrk8AjMfLwA2eP3oI5cKqNPbWR6TrDpu0jPZLGORi0UYXBDFgCDgcDg5yR8IbPLieXKoLiyaKPZjACrk45EDnnYyeOeZPbisYsOlS7vymzjJbgvDJ4yEDlkDPDvOO0VQ9I+mwxshyCcrs4yTnmdnO1kkrheXeBWWxLhmbliIKN7okdKLxY+YyMDDt5J4gEr5KkKg44O1x44HDNUHRqEsd6fgEZiHZ2gvjjjOeGc\/WRVoisnu5GmKmO1HMHCNJyPEbR2UycnOOZGB25hpdvsjgAOWAOAUAAADhkjh21nUajHZRxwTqT2nw5GH9MLALdLKIxIpVXkibaSOchmDK7xYdCwAG2pyOfHlW2vcR067tre\/sJ57aC7iilRHMcwiWbZJD7ShiUyQRnmprBulVvnYb0EfWCP0mt\/9Sal9BtQ\/YLpB+Sl3OF+gAD5q34KW03F6FR0p0VhmttwV28\/E1dqfg3yLxt7\/AGxj\/eQEfSY5jj6Kxq96iNTVgkMkNwS2yMNLGeIzkh48KvDnmusVUgL6B+jlUGnRkuzk\/wD2qw3MTz0+h8NLlb4M4z1Lqp1mA4eyZxxwYnglBA5kBJNrHzVbIuhGpsCy2E7KuQXEUhQEcxt42eHy13BcQK33vHtJyeHaME4wf8KikgBTZKgqo8lcDZXHLCDyfqrHcI5pdA0XwbNL+BhnxvUcfFof1rV09hu8VzF4F5\/leo\/1aH9a1dQbY76Yf1DHoP8ASR8fqyDDd4qRqAbdvxHwTVVtjvqn1Bxu34\/emtxbk20+An5K\/oFTKl2vwE\/JX9AqZQChpSgOfehFjfBrLcw38N+lxenV57o3yafLp5N1uY0jupN3LOc2uxuUyCCT31kHg5WtxGjC+juEuTbQ7Zuotahw4J3oeXUbh7eaUkofuCR8A3DHAbioa1qmk7nDSwKpyjK97X80l88u\/mco9OYNMfphqS68diw3cOWzOn3YWFluhtW33Tz\/AEVdOoJo01\/UYdDeSTQfFnZt5vNja2YhEzbwA7ze75ULAMUDc8E1sWXqlEuv3Wt3m5u7K6jVRYzR70rIlvb26uRIDGSDAxB5+VU7q16tp9HvL3xW4V9GvSzJZOJDPbyY+5sj\/BIUFoyebKEJ4rWmNN7V7c38f\/hV08DWVdTcVbeSd161ne13f1X8zmzqxt7FoJDfaPdaxIJfIns2uljiTYT7i4t\/J3m1tNx44YVsnrfn3GmaBoemo+k2OsEvPDM0rTQLPJCxt7lpW2yiyXsjuhI+Ao4AYq8dE+pzXdOjaHT9YjtYpH3kiJGWDSbITbO8jYg7KqOHdWY6\/wBV0mp6VFYa1deMalbySyRanGigo0jtsqYsKHi3ZRWXhnYByCAaxjTlZq2du76mrD4GsqEobNpbPG0VfNNraTvmssy3yeDzpISAQmeCeB43a6SZ99MYyCcgjYhY4yGiVCpwRyqwhAOnijutMDJJJxYEfCbix9NVlt1V69IYYL3Xm8StnR0MG8F026IKZdgpZxgYMjyAHBw2Kv3Wl1Vy3t3b6vpd2dP1W1QJvmG0kwQMqsSg+5ybMjqfJZWU4I4Vs2crqNs0dcqD2VKnR2bTi7ZXdr3tZ2y7+JivWEP\/AM8aN\/VIv06jWCm20d+kWtjpE2xbCecwHN0h3++AOPFfKJ2NrgeFbd6u+qm5g1H7Oa3e\/ZLUEQrBsArFEWQxFySF2sRs4CKiqNpjxJGPdG6nY\/spqeo6kIb+11LemG3eMtJAZpA5cO48iQJkbSYIzwIrF05Plzvmap4OtU9LZWdRytLNJbNle38GsOpu1upo+klnoMkn2OeGRdOeQtEfGGkIhEbtgRXElttgt5J4xFscKxjRLLSLWFbHpLpN5Z3ZZ1bUUadNoFiQy20mzHsqhA8hZdoLtcc1vToV1XX1haalpUWo7Fje7ZsZkV\/HLF3IVmztKp24gA2wy4YbS7JY1Y9X6ptevY1sNR1pZ9NDoxG6DTtuz5LEmNXdvy5WGcHjUOm7LL6WNEsDVVOHoNySeTUXHN3tZvJaNcvkWbrLdda13StAWcxaI1tDPGIjspcK0El0siZ4OTFFHGhYHYy5HEkGT159XdroUFtrOhu9hdQXMcWzvJJRJtq7h1MrFtr7nhkzsMpYEd+wenvUzDdRWJ0+dtOvtJhigtLsZctDb\/zSylCrB1bLCRSCC7cDkAWJOprUb+aF+kuqeP2ts20lrCGQSd4ZtlFj2gMMwUuQSAy86mUHnlnyehvrYSrJzTp3lJpxndejw77q3dxMR64tOurm\/wBP1u90+bVdHlsbZzaW7ToLdpoTJPG5gVpICJHWQsQA+FUtw4Zr4Od3oTz3h0WKWyvJI4\/GLK5YyMkMLsC9u5dyybyUBsuSDscF7b9086E6xJeG90XVvEEMUUIsZEDWsaxA8UUh4ySWJyY88cbWAAIeqbqwlsLu51fUrrx\/VLxSjui7uJFdkeQ8htuxijGQqhQuAONZKDU72NlPDVIYraUeMm3KSjpxi09rwaMa8M9c6XaD\/wDcF+b+S3VYb1zdZ0moaUbJ9LuLJS9u3jM6sIRumBAyYlGW7ONbj69+r+XW7OG0gmS2aG4ExeUO6sohmh2QE4g5lB+Y1W9bnQyTVdMOmwyLDIXgbeyBmTELAsMJx44qJwk3K2nzMsXhK851XB2Tgkll6WTyz4Gkus0DddBvRDa\/p0ysn8M8ZtNOHP8AlUnDn\/uqyzpr1Si\/0rTtPafcX2kwwpb3iKWTbjijilBTaDbpzEjAghlKKeOCDj9j1O6jd3VrcdItTGoW9gwaG3jB+6bJVtmR2RAoYxptHDMwGNoc6ShLNW42NNTC1rVKai3tqGd1ZWSTvnflyuYR4St1cQ9I7S5sc+N21hDPFsgsR4s95PKSo+FHuo5Npe1dodtTeu7rS+y9jb2OlqxWS3F9q6j\/ALukB\/2WRjgbCygOT2\/ccZ28Vt3X+ryWfpDZa+JUFvZ25hktirGSQmO7jyG+Ds\/ytefmmqFupe0gs9VttNJjn1dSgknw8drFt7xbeERoGW3BJ4HLHC5J2Rg6cs7c\/wA+wqYHEuVVRdoybvrZJWt8eD7iz6F0sGldDba8UgT+LGGzBxxuppJViIH3wTypCPNjatUdUOqLoupaZcm7iuYNYt93qKRSxTPZyTyndLdLG5MciE2rlnwfKnH3pra+q9TVzc22jabcXUZ0\/SeN3EgmV7t3lLSbDcNgbnyAc5G8c91VfTjqB02e1eLTIRYXhKGK4aS7mjADDeI8ckzAhk2hkDIOKhwm81yX5\/oirhcVNxnFL0Ixsm821Zy4ZZ+rmYH4UNpcS69pyWGfHfE0a12CEk30U9zKm7Y8pMpw9OKsHSrrA+zFx0aaZd3qFle7q\/jwVXba6sd3MgPwQ+6kynNWVhyAJ3HJ1Z3kuo6Nql1dRySaTbRQXWFl27qSEzZmUngpYSqTnt2uzFU\/T7qUS61W21ixkW0ZbiGe+hZWKTyQSpJvYtj+blcIQ3YThuZYlKnJ3ffw+RjXwWIk5zh\/3TV4vRbLuu9NNPuMR6Qad9sHSu40vUJGXT9LiLxWqMU3mwsG1jueR7gsXA2thQoI4EUHW50Xh6MXmm6robPb76Vo5rMu8omSMxs6AyEu8TqxVlYnBKFcECtldZ3VM15eJrOlXR0vVUADygMY59ld2rMUO0j7vyCcMrKACvbVr6P9Tt3Pew6l0l1D7KSWhVre2jBWAOjB0LkhVEe0FYoqDaKjaJGQUqbu8s78TOpg6jclsek53VS6yV1bvVllZIwHra0KWHXrrUda02fWNJmUC3aB50SCNUj2MyQfzW7ImG6cxhi7Pk9uz\/BxudHa3u\/sCJItqZJbq2uTtTwF49iFQ2W24PuUhU7THJfJ7B50q6D66by4vNJ1rcRXbhzazoHjtwqLGEhDpLGBhRxVEJPE5OSbn1L9Wv2HF1NPP45fag6vcyhd3GAhdwqKSSSXlkYscZ4cBjjlCLU7210N2Hw1SGKclDJuTbaV8+Fmnd30ayNi1LuWwpqZVDrEuyh+SuguzGydqVmPyD5eZ\/wqk3mLi2Uc2dx8vkOTUdoucue1iR8nAf4fXUqKzU3NvLk5iZ+Z8nykKnhjnVU5bVa\/eXVGCjRafsvzTM6jVsDiK9w3eKlNdooGSOXeKt1xra52V8o9gHE\/QKtSlLlNIVGSRVh1TV2+Ahyx4ADmakzzSzEgAxqDhmYEenAyOPCp6aYqjK5L\/fMeZ\/YKi5KjcodPtsynxjypNkMg+9AJIJHewOPpFcz9IejR03WLqIDCvcyzoT9\/DfM1wGHeAZXTPfG3dXVTxZCuPhR5z6Ub4Q+kA\/8AlrXfX50Z30EepxDMlmNi4xza0dshz37qQ7XcFklPZXDjYOcMuRadG1FTqWfPLx5GvmgV1KsMgjiOPEcjVi1bRFZSrKJEzxVgC3E9g4DI4c+6r7psmVXPyVPuV4ZHz91Uik4np3BSWZr4dAYHJ2QUDcx2DtGBnhgZGMd3PFVmndAYYvLIBbBy2BxJOeR5nlx9NZLK+Pm\/12VLbUgoI7ccAMc8dueQrN1pPmYxw9NckWW7s1jxGOQ7PSPl445VLjj7Owcvk7KmysWJJ41KzypcztYoNYiDAA9nH\/Xoro7qs0ow6TYxMME24lYdoa6LXJB9IM2PmrSHRfRDfXkFmPgyuN8Rw2YF8qZs9h2FbHpK99dSboKMAYA4ADkAOAA9Aq0wEOMvAo+lanqw8S2Sx4Fewx4X5aqb1PJzXhHAfNVoimJKwV6I+Y\/1xqrVagZeJ+QUINA+BeM3eo5+LQ\/rWrqDdjurl\/wMCfG9Rxx\/k0P61q6e2m7q0Yf1Ch6D\/SR8fqyLdjuqn1CMbt+H3pqdtN3VI1Bm3b8PvTW4tyfafAT8lf0CplSrT4Cfkr+gVNoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAVj\/AEqucKQOdX6VsDNa96W6iwb7nG07DiETZB4ciXZgFGSO3PcDWutU2IORtoUnUmormVuCEUeipMhZFMhwApBJJAAGcFiSOAAJPyCrXbyXcoAdBbDu22kf6VwPpBqpXQNvG9YyY7HJYZ9ANUkKrTukz0Mqcdlxk0vMpdb1tVOx4xBt9qiVJGXHYUQls+jFe6Nq9uiqXmDvtZaXYnVRkgAK7xhQB8vfVwj6NIOQAx6Bj6OyoNT06JI2WQAp29hHbkHsNdTxlXjso5IYGheyk2zL4pVKhtoFew5Bz6eHCveHNTkVqTqw1ee9iuDLJiC1uprS2EW1EWhtCIhLNIG25JWYNniFwFwuck5Zo968VwtvtmWOfb2Ucl2R41aXKuxLbJVGGySRnZxjjnqhiIysaqnR04J5rLO3cZfGMH5amNCpUowDIwKspwVZSMFSDwIxwxUmF8ipwat5wGiOnHRZtOmwgLWczHxaQ8dnmxtnbz1GcZ4sozxIbFgM+Pn7K6Q1SxiuInt7hBLDIMMpz8oKkcVcHBDAgggEHIrSXTfq\/urRmltla9tOYZBtXMQ7poUGXA4eXGD25VAMmnxeCcfShw00PQ4DpGMkoVHZ68n9zE7nB5fw\/wBYqhfZ\/wBcOPz1OSTaGVII7x5QyOYyOGRxqguVPzdno+n5qrLl5skM3E1IueA48AO39PPgOVVOn2byMqqC7OcKigszN5qqvFj6BW1eh\/QGO2K3+r4UIymC0wZDvOaNMqAmRxjIjUHGMtyIHTh6cqjy4c3yObE1oUVnx5Lmy69RvRE2sBvrhdm5u1AjRhh4bXIZQwPwXkIVyOwBAcEGtg3R5D\/XClvepIgliYOjciMjj2qwPFW48QcEVDIeI+SvRUoKEUkeQrVZVJuUuJDKMqakkeSp7uFVC1KVcqR3GtqNREteSDiPSCK9Q8KS8ge4j6+FQyTn7wLv9r1H+rQ\/rWrqGuXfAwXN3qP9Wh\/WtXT26FacP6hQdB\/pI+P1ZMqn1D+bb5DUzdCpGoRDdt+Sa3FuUUNy+yvH70d3d8lR+NP531D9lU0PwV+QfoqOoMbk7xp\/O+ofsp40\/nfUP2VJpQi5O8afzvqH7KeNP531D9lSa9xQm5N8afzvqH7KeNP531D9lStk91S5pVX4bBPyiF\/Sai5Fyp8afzvqH7KeNP531D9lWm41y1T+cuYY\/wAuWBP+p6tl30+0qPhLqdpH+XdWa\/plpcjbWplPjT+d9Q\/ZTxp\/O+ofsrENO6xtInmjtbbUra4uJmCRQxTQyySOeSoI2OTwrKaJ3ClfgTvGn876h+ynjT+d9Q\/ZUmrfdaoFlNusUk0ixpI+7EWyiSvJGhYySLxJhk4DPKpMrl28afzvqH7KeNP531D9lW+TUYVXbaVFTEh2y6BcW+d+donGI9ltrzcHOMVKGtW2w0wuItyjBHl3kW7WQ4IjZ9rAcgjhz4ihFy6+NP531D9lPGn876h+yrNHrkLTJbI4d5UEkbLJbMrqys4KoJd63kqDkIQQwIJAbZqbDUYZiwglSYxkCQRukmwTkAPsE7Jyrc+491BcuHjT+d9Q\/ZTxp\/O+ofsrHD0qhEZmdZI4t1NNG7KuJ47ZTJLuijnywilgrbJIBwOBxPbX4l2lkV4pk3Y8XZNqaTfs6Q7pYiyy7TRSjyT5OwxbZAzQnMvnjT+d9Q\/ZTxp\/O+ofsqxnpBEMqwdJw6R+KlD4w0kqs8QjRSVkRlilbbUlAIpMsNhsTrTVkd90ytDJu2l2Jl3f3NHEchVslXCFo8lSRiROJzQi5dvGn876h+ynjT+d9Q\/ZVqfVoxbeO4bc7rfgbOJDGV2x5BOQxXHA458cVMtb4Mdh0a3kIYrFMYRI6R7IkkRY5GyimRAT2Fh3iguXHxp\/O+ofsp40\/nfUP2Val1m2LJGLiIyShTFGJIi8gkXeRlF2suGQhhjmOIqXd69boJTvkd7fG9iWS3EiEuIwr7yRVjO2dnyyvHhz4UFy8+NP531D9lPGn876h+yrSmt2+zEzTJH4yMwB5IQZQSFG7w5EnFlHkk8WA58K9v8AVFjdYdh5pWRpN3Eu2yxIyo0jZIGNp1GBlj2A4NBcuvjT+d9Q\/ZTxp\/O+ofsq1W2p7cjxJFIVikaJ58RiFXVQxGTJtn4SjgvM1X0JuTvGn876h+ynjT+d9Q\/ZUmlCLk7xp\/O+ofsp40\/nfUP2VJpQXINRvHCHyvqX9lY\/bXqqm05G0xYknA7cY+QACrprTYQ1j9vocLqJJFEjtxy2W2c8cKDwXmeVcOOlJQSjqWPRsYuo3K\/AifWeOIlEhPbnh9XOpc2qXJ+Dsx\/kj6iXDD9FVyrBCPKZYx2ZwozzwOWfkrx9Sj5RRtKe\/G7T52kGcekA1VJS9qxd3hfKF\/iWiOS6k5zMPk2f8FqwdN5bmGL7pIZImwCSIwSSeSsigjGORPbV51\/pI1sAz24w3D7m+2wPcQyLnt7eysL13XH1ArbxYRGJ2g2fIZexwBtL3kYzRRvldnVTTXpbKS8DUnV71mTaddahp5AeFr64kjY5xtSyFnUOO0MTwPdXSfVDDNcFtUuQUWWPYs4zw+5uQ0k+O5tlFU9o2jyZSdSdXPVjtX8aXS5V5GkmYYZJUUmWUrKp+\/ORxw3ljgK6XtFAZlUBQuAqjAAAHAADgAB2VZ4Wkm9tnB0li3GO6XPi+7kiYw2W9B\/xqah5j\/XGvHGRUoPgqT28D8td5RE9WxUaSf65GpLmpbGliCh1zotZ3ZL3Nukkh5y4MU+OQG\/iKyY9G1Vgm6sLAnIjkA7hLKQfQSxLfXWVCQjtr3emsJUISzaXyN8MRVgrRk14lBoPRq2tP9mhWIkYMnlPMw7jNIWk2fRnFUXTw7MccwG0kDNvBz2VcACXHcpGP\/P3Zq8vKahCgghhkEEEHBBB4EEHgQR2VMqScdlZE0q8o1FUeb7zSl1rlxDeNcWbBU3ah0I2opyrO6mWM4yQGC5GGxwDDNZrpvWEmVF9GbYkD7qgeWEk8PKQDeRcTyw4HaRVH0j6KNFI01opkiPEwAbckXIndj4UicMgDLDlgjiLQJI5gvDb8oK2MZReTH5cZPy4qp3lWhKz+xeTpUMWtqKz7uK+JtiCZXUSIwdHAKOpDIyniCrDgR6RROZFab097qwO3ayeRnMkDEtBJkngUzgNjZ8pcMMc8cK2D0Z6YW90yx53Fyf+7ycCx5ncvgCbt4DDcCdkDjVjQxcKmXB6f6KnEYGdLPitf9mSDnSf4J9Az9HGhNDyPyGuk4zn3wMGxd6j2\/yaH9a1dP7z0GuYfAu\/2vUf6tD+tauoa04f1Cg6D\/SR8fqyDeeg1T6hJ9zbgfgmquqfUP5tvkNbi3LTD8FfkH6KjqGH4K\/IP0VFWJgK0n4XnSe80+wspdOuXs5Jb0xyPCdlnj8XlfYJxy2lU\/NW7K598OQf9maf\/wCI\/wD+W4rGfA0Yl2pya0NA6P0x6QajcRWNrqF5c3VwxWGBLmaNpGVWkYAmVVUBUYkkgAA1PtdP6R3N9caQs15LqNmkkl1aPesrRRw7veNtz3YicDfRkbDHaDAjI41mXgoWEVv9lOkl7MLK20u18Wt72RHmjhvtRIhWURR+XI6KY12FwSLkDtrb2m3Vg2q6V0qivI7i11Szn0PU9Q2Hs4X1SNU8XuJILjyrYTG1liG8PZbqC22CdSjdFfRoOcU5SefK\/I5j6O9E9U1S2W+jlMto+oW+mb2e4kIW9vDCsKvGS0m6\/lUOXCnGT3Gsd6ZdG5NOvLjTrsIbizk3cxjJeItsq+UZ1UsuGHEgV0LpWhno3osWmarcQC9vOk2lXdrFDKsv8ksrqweW6kJA3UOxaOSzDC7SZOWwLd1ndX9nqev3GoDXNLNje3lq0lsL6A33iuzbxXnkABFkEaTOBt8QBxzUOORE8L6Ct62V8zVnTLqrvtN06x1i7SMWmphNyIy5mgM0XjEKXSNGojd4w5ABb4BBxwzV9WXVkNXtL66ivore506O4mbT3R5Lma3tYY5fGF2GAWFpJd0CQcMrc8YrfXSvrF0HWG1nQHu5LaO6jK2l5eNYxaHbXel7MVq2nypJvBHLIoky\/B1RsFQwB014NPSO1sLzUTqMy2sNzpF5bK75dWnkkg2IlMattEhXwRkHZqdlJmMqNONRc08uPNGPdREmNe0g999APWbZ\/wAa+g9fO\/qWfZ1vRj2\/ZKxHr3Ea\/wD1V9EKzpcDp6O9VrvFWyfQ4nuDdyqJWEUUcYZQ26MMk0u8RjyYmYeoKudY7qGnSS3xYqGgW2t+Mm\/CB9\/cmXdbshTLsbvOezY7K2FiSr3ooZFkia4xAyX6Rpuxtp9k9sytJJvPuuw0jbIATgcNtHyqrdQ0MvP43FJuplMRi2kEsStDHcwHbQOpdWjvHHkshBVeOMg2OK\/1FvGMgxuAwVN1JII38YjSJrcmySOVNyZGYb6Yngcpgiqu\/nu41uiHllCXEEFuVjhDbg29s813mKxlabMrzqSkTqDkbIC5QCpn6NM88c8lwzrGyPusOo21t2tmWPZmEUUTbbSYEZYOx8vHkiq0LRTAyM8u+3EC2sGEWHZt0IYb3ZYiWYlEywCKMcEXJzYWu9Ra3eUF4pobF5UiEKuLi8jlnVInWSFXYPHFESkYjY7wFdjOKqNauL6HbjiZ54xNHm63aiZYZIXZhGtvZTLIBOiLlYHKiTB88AVa9EIhbSW207SSW81uJ3eeXdLcqyO0EEkpjg+EOEezwAFRnowod5Y5XWQvDLDJI0l1LDJAkkWzvbmRnktmSaUbokYMspVgXGzRLPfYMrMwMUViwgSEbq4eWRhdhzLCJsiMJlV2Ch4kY4VQT3V7DA0cbXE1wLq++6PHtBYxNK9nGrJps2+hkjaIgqAq8U3kWFQAX5+j7M\/jLz\/y0NG0cyIFgjWFJ40hFs0jFoSt5c7WZCxMmQy7K7MWr6A1zHGk833RHbeSRIIlktplMdxZiMuzJDJGcZLMwKq2fJAqn0q+uWuwsok3TxqdgRPFBATBG7CSSW1G8fe7wZS4b4QBjGyzVBoF5ePdSJcApEGnGwVl3YjSXZtWgmFmkbM0ZRmBuJT5TeShBUAXTUdPe4hubaZgEuNuOIqvwIHRVAdScO+1vD2DBUdlSbnQVDRvZlbRoxMmBGHjZLrdb37mGXEv8nhKvkgbPFWBxVpZ7o3Zu93KsDSm0AB+BZn7kl0LNsnfC8+67wpjcNk5Aq2\/ZC+hs7NYxcTXMdsDcGWN2aS6hWMSW8uzp7tKM7wCTbi2xxEshJYAZDZ9GBHDuN4Tx087ZUZ\/7L8XKZGfvzbZ\/wCHbOM1Ki0OfdyWom3VvEyeJsFIkKCRZ3S4eGdXljyux5JiLKfKJPE0Isbkzlo2kt9ldYYOsaPtF763e1Q7+Nk2HVWYAAFlB2SOJqdDqN40ilhJG7PF\/JtwTZi1eBHnme53e0LhJDMN3vFOUVdg52yBEnQ7EUkCz+RdRSwXeU22kgmnubnELNJmCUG9nXbbeZGCRkZq59JtF8aAXaRAAw2niE0sRcY3trKJEa2uAOT+UOA4HFW7Sb67NpcMQ8l3GuYmlj3SyOUBO4gktrZyFbJCupySF3jcSKGGe4Ekzo1w0Eklqj3T22zdrAsNyzNDam2AkInMCEiAkK5Ozw26Autt0daKeW5iMLPIzuJJLfavQzRCNV8eE48nKr\/u+RI7c1frcNsqJCDJsrtlQQpfA2yoPELnOBVJ0fllaBGuARITIMsu7d4hI4t5JIv91K8IjdkwNlmIwMYFfQClKUApSlAWrpBJhDVjhtZnVQj7pcDylwXIx2EjA+WrtqY25Y4+wsM\/IPKP1CqrSplKI3eox9ArhxsVJxT7\/wCCz6Om4KUktP5LZa9HwCGYmRhyaQtI3efKbJHZwGKrY7ZYxy+bifoBqn6S9IFgXgNonkoKg\/PtHly5ZPoNa01TpVezzPDA62hC+Q5QzFXIODiXZzjGcFRxrjtGPAtqcKtVXk7Iu\/WOzPsRHAAfb4448xwHPtrGtKtUEzTr2IUbH3xyNgnsJCs\/0r2CodLE272riTxiRS4k2w+2rFmy5be+Xl+PIcGGOVTlBRdkeSBxI4c+ZOe0msYqzuWKSSS0M36tI9q5kfsijx2jypGwpPneSklZ3bny39OKxbq3szHbCZhhrljJx57oeRF\/5ThnHoesmJ4576uKEbQSPM42pt1W\/wAyKsGpM6+SR6cj9Neq9GPA1tOQghl2h6e2vGaqFJdlj6aqGkzWVgRs1Q7VSduvC9TYXJwNTM1TI1Yr0g6yLG1mFozvPc7axGOFGdUldhGEedsRKwY4IDErg5GRisZzjDOTsZwpym7RTfwMsjOWrA+snSI43W4hcQzTSbLwkhdt2DMZ1AG0pyh2uwk5552qe+sry5O3PM6JIMiCItDCin70qmDOO3MhY\/JyFBb9Ed2SRwBOeHM\/lemqjFYyM47KjfvL3BYF05Kbml3LPwJFjE5JWV9sDzVkAPyOwC\/OT21TdIY4kXLMobgVAILLj74sOAbuxywePHhlFtpqrwYdtTm06AeUyAjvIBPPsJ5VWWbLZzRV9WXSjxyJopW2ri3xlj8KaE8FkPe6nyWP5JPFqzAVpHWdTWxuori1OGBPkE4VhyZD2hGGQefPI4ityaVepPDHcQnaimQOh7cHsPcwOQR2EEVe4KvvIWfFHmsfht1O6WTNEeBgP5XqPHH8mh\/WtXT2we+uYfAwbF3qOfi0P61q6f3o7634f1DyPQf6SPj9WebB76kagh3bcfvTVRvR31T6hKN23H701uLctkPwV+QfoqOoIfgr8g\/RUdYmArU3hO9A7zWbG0ttNVHlgvBNJvXWFRFuJoshiDk7UicBW2aVDV1YwnBTi4s40g8GjXSuwZbaOMnaMZuLgrtctoolsVLcBx9FVtr4Kupn+dvLSPPPYN5Kf\/dboD9NdfUrHdo5uo0u\/wCZynbeCZP99qcSd+xbSv8ApnTNXK18E1P97qpb8i1Vf+u6NdNUoqcUZdTpaHPFv4KViP5zULhvyEtY\/wDqR6uFt4LOkLgvc3kneDJZKp+ZbPP11velTsIy6rS9lGqOjvg+6NaXEF5CkzT2k0VxC0k7lRNbyLLGzIiqGAdFODwNbXpSskkuBthCMfVVhVh1TWJEuvFY8AbmGTa3F7d5aaWaLZZrXyYFAhB2pMA5PYpq\/VQXukRSSb5ttZNlULRTXdttIjM6K4t5UDgNI5G0D8I99DMopeksavLFupGlgdI5IwI872dxHaxqzSBGMytvAc4VAS5Q8Kl3PSuOORYJYpI5SEaRD4sWhWWRooywScmXJjZvuW2QuCcZxVdLoVu20WiBL5LtmQOzNIJ9tnDbRdZVVlbOUI8krXsehwBkdVYPHgB95cbUgV2lUXDbzN0BI7sN7t4LMe00BHoV400W9cANvJ0wuQuIbiWBeZJyVjUn05quqms7GOLJjXZznIy5XypJJmIVmKhjJNISQMnOOQAFTQClKUApSlAKUpQClKUApSlAKUpQClKUAqCZ8Amo6s\/SS+EaMzHZVQSxPABQMkn0YFAQaSN5cM5+DCpOR5zcAPoJqm0Q7UcTdhRT9Qrh3Uun+o6zr7R6ddTWlvdyeLRi2muYD9jIjtzMdzIFIkVGcnGTlRnsPbvRWUG3j9C4OOAyvA4x6RVbjJLbiu5\/wXGAptUpS71\/JI6WQps5CgyNw23G0Ix5wU5y3d2d9YPcRKrAgfBHA9pxk8fTkGs+1zBUnGe75uR9JrX96+CfTk\/MRgfVXPJ5l5h03CxRahdbqUOPgOGDA8jsgYPoyh\/9tV\/RmyN\/LuV4W0ZBuJRkYTOd0rD\/AHjDgO4cezjTQ6Qb0qhO7gj2TdyjBMcByrbIPAyMFIGeAwSc4wdr2GmxWqC3t03cUXkqoyT6WZj5TyHmWJJJ510YejtPafA5cdjNhbEeOv5zLyEGwFUbKqAFA4AKBgADsAA5VT5xwqK2l4YqG4HbVkefPVepm3wNUAkqdHJWViGW+8fDZqOKeqLUZOJqmW4xWaILw8lA9W+O4qMS1JBcUf5vT3emubrrTZhP4s+VuISxkc5JWVGGDk82Y4YHkRxGRXQW\/rHelw2P5XFA1xLsbD7pGllbd5Nv9zQF2UGSbiAcZHorix1HbhfQtOiq6hV2XazMb6O9M2KiOXjIMrhQ7SMUOyQEUEjHD6qvSarO\/HYWNe3eP5fzJGrfQSDVm6udIZYi93GLfUbiSWadG2C+w8ri3ZyhIH3FIhs81wQcFSKy2TT4oxmaRV5HymUfNxNUcozWRfSnS2vRLbGJWbadgy8dmMDZX5WJYl8dnIceINVTQyNwLKg+Qsc\/SAKgl1e0RhGJVZuwKQ30sOC\/PipyagjD7k2fpz3c+Xz1rtqZp6I1Z1kwOk+w\/Fl5HhxVu0Yq+dTXTQW7\/Y+7bZgnbMEhyRDcPgbDd0chxx7G4\/fki19Z0hadF5nHEnu4cKwy7jxw7OznxHd6azoVXTltIjE0FVhsy\/GZx4F3+16j\/Vof1rV1BiuXfAzbF3qJyAPFoOJ4DjKwHH566fBPbivQYf1D5h0F+kj4\/VkeKk3qZRgBzBqfStxblijGAB3Afor0GruVPoqXApweXM1FiLFtpV22T6KbJ9FLCxaaVdtk+imyfRSwsWjNe1ckU5PLsqZsn0UsLFppV22T6KbJ9FLCxaa8zV32T6KllTtDlypYWLbSrtsn0U2T6KWFi00q7bJ9FNk+ilhYtBNe1cpFORy51M2T6KWFi00q7bJ9FNk+ilhYtNCau2yfRUE6nHZSwsWylXYKfRTZPopYWLTSrtsn0U2T6KWFi00Bq6upweXKvIlOBy5UsLFrpV22T6KbJ9FLCxaaVdtk+imyfRSwsWd24E1pHwndauItJuzaRiXa3cVwzlljit7mRYHLGORJMtt7I3ZDDJPAKSN93AYIeXbXE3hK9KJ7nWrrSY5THZ21nHHPGCRFJeXAd4JJV5MI1mLAnlxPZU2CSXEoPA26IBpr7U2UHxcrZwfCwrFVnnPH5YOPobvrqfTW3SEfe8fmJ58uVc2+Bn0ojt47vRbw7m8W5aZY5PJZ9tFWQcebAxj5QRXQtzeBdrjgHjw\/Tx5\/69NedxLarNs9ZhIp0IpcOJHqmsIFIz38OHH0Vrq\/1QO5weGck9noGa96Y9I4Ic7SGQkZzgxx473ZwVxWiOsPplc3k0WkaZwur+VLbKcDEZ3WNYY8cEY7Y2nPwQRyJyuyhCVWVjdVrQw9PafyOtOi+mtDapK+CbwB25MoRl+5RN2Z2GLYP4Rh2Vk2l329jVz8LGy454kQ7Eg9ZTVBcOIk3WyTEFCbIwVKoNldnlyxwxWP9B9XDSXcQIeITbdu4++jkGzKrjGUmS4juEZTggrx51cqKikkeenNzbk+JnkMtVDSZFWpZamRz1mjnaI5zg0hmqVPJVKkuM1mjEpdRl8oirXNccamXU+STVtuZKzRBdIbmpxueGc1ZEm\/RXslzgVJBdXvvTUxbvhWMC6yarVueFDKxW6nsSJlsq6EbMinYdQzKHG2OOwRzHoB4EA1WWmlxDIXBx2jBJ+UniT6asNtcglkbylIIIPIqRjB+Y15bWtzH5FtLGIWOdqYSvMgP3qiMgMB6SPkFVuMwznJSirltgcVGEHGTa0L1HYAty4fV2\/wqs3CpxOAO\/8A18lWnxGdl4XQVsDyhDwyOfDf57+2rdfyyxI\/jMykRdo8ksDxVsMeRGOAzzxVfPDSpq7RaU8XCs7Rl9S1dZKo0iFOYU7RGOJ5j5q1xqgB5fLnn\/rhV\/1dnmOVP7MGsfuoyBstz7v291cjWdztja1jDNIvZUhuLdDiK73InHa627mWNPyd4Vb5UWr70d6d6nY48TvZY0GMRM3jEGO4Q3AZFHyAVRdDZhHd2bchHdWrZ\/InjY\/orfHWV1UtfatPLCsemaakET3N6ypHFviHaVkjBUSSbOwWclVHac8Dbwi2ro\/PmEoV6kNui3dNKyyyd3x5L4mOdHPCRvYsLqFrHdqOckJa1lx3lTtxu3yBBXQXV50qTVLOPUIopLeKYuEScRh2EbFC67t2BjLBgCcE45cq460vod41qcelWk63aSzBPHIQ4i3CjbnnVZFBwiBz2gkDBIIJ2trvWJqvR69NtcWi\/YfO7061JQBbO2VIY\/FruLP3TZEbOsgbDPyXINbqdVr1uBd9G9J1oJzxDbgna9r2fe1y+fFHR5NS4CMH5TWE9XvWfpurER28m5uyMmznxHOcDLbriUnAAJ8gnAGSBWaQRDB4dprqTTzR6elWhVjtQaa1RO2hTNQ7od1N0O6pNhFtU2hUO6HdTdDuoCFGG03zVM2qkpEMnh3VM3Q7qAi2qbVQ7od1N0O6gItoVAWG0Pkr3dDuqAxDaHDsoCbtU2qh3Q7qbod1ARbQpmod0O6m6HdQEMrDI+Wpm0KkyxDI4dtTN0O6gItqmah3Q7qbod1ARZqXcHhUW6HdUE8Qxy7qAm7QptCod0O6m6HdQEW0KbQqHdDupuh3UB67DB+SoYW4D5KNEMHh2V5FEMDh2UBM2qbVQ7od1N0O6gItoU2hUO6HdUMiKBkigKS9lAQ5Paa4q8I7QwmqXN\/D5SXJg8ZOP5uVIkij2j5jKBgnGGJHaM9QdPukUdvEzNzLbKL2u7cAB+n5BWpLK335d5VD78nfK4DRurHDIyHIcEbQweHHBrgxeO3DSXHn8CzwHRzxKk27Lk+85gvLoBkkcZlhxup1JjnjC\/BCypxZR2K+0B2AVe7brX1GJdgXbOgGNmZIJzw4D7owDD5q2t0w6mLOcmS0lexcgnY4XFsCe0RyESAk\/eiQKByArWurdS1ymSt1G6j74xvH\/wC0O2Oztp17C1c5Wv3o2Po3G0co3t3P7r6GP6x1mXMoO0QzN98ESNgfQ3lbJ9IwR2EVfPBxlih1SPVb1GNvZ7zYMYDFbmRDHGSrEF0QSSSEglshDhsnNlsOrVt8FuJwY0YFxGrbbqDxUFj5GQOeDjurP44kiQRQoIo1+DGgCgZ4nhzy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width=\"302px\" alt=\"ci\u00eancia de dados impressionador\"\/><\/p>\n<p>Al\u00e9m disso j\u00e1 estamos lendo essas informa\u00e7\u00f5es e armazenando na vari\u00e1vel base, com isso podemos utilizar essa base de dados para visualizar os dados e fazer os tratamentos. N\u00f3s j\u00e1 at\u00e9 temos uma publica\u00e7\u00e3o aqui no blog falando sobre a Introdu\u00e7\u00e3o <a href=\"https:\/\/www.florestanoticias.com\/2024\/05\/07\/como-a-ciencia-de-dados-e-o-aprendizado-de-maquina-estao-revolucionando-o-mundo-dos-negocios\/\">https:\/\/www.florestanoticias.com\/2024\/05\/07\/como-a-ciencia-de-dados-e-o-aprendizado-de-maquina-estao-revolucionando-o-mundo-dos-negocios\/<\/a> a Ci\u00eancia de Dados, falando exatamente desses principais passos que voc\u00ea deve seguir para criar um projeto de ci\u00eancia de dados. Agora vou te mostrar os principais passo que devemos seguir em um projeto de ci\u00eancia de dados.<\/p>\n<div style='text-align:center'><iframe width='564' height='319' src='https:\/\/www.youtube.com\/embed\/PYIzXPvLlms' frameborder='0' alt='ci\u00eancia de dados impressionador' allowfullscreen><\/iframe><\/div>\n<p>Aprenda a estat\u00edstica por tr\u00e1s dos modelos de ML e IA, realize an\u00e1lises explorat\u00f3rias, treine e teste modelos cl\u00e1ssicos e  redes neurais, tudo isso com Numpy, Pandas, Scikit-Learn, PyTorch e mais ferramentas Python. Por outro lado, os profissionais que se tornam Impressionadores Python t\u00eam a vantagem de se destacar em um mercado cada vez mais competitivo. Eles t\u00eam a oportunidade de trabalhar em projetos desafiadores, ganhar reconhecimento e expandir suas oportunidades de carreira. Ent\u00e3o n\u00e3o tem pra onde fugir \u2013 se voc\u00ea quer se destacar na sua empresa ou em processos seletivos, voc\u00ea precisa dominar o Excel.<\/p>\n<p>Com isso voc\u00ea nota o qu\u00e3o importante \u00e9 o tratamento de dados para melhorar o nosso modelo de classifica\u00e7\u00e3o. Para a \u00faltima etapa n\u00f3s vamos retirar algumas colunas da base para  fazer nossas an\u00e1lises e verificar os resultados. Al\u00e9m de avaliar essas informa\u00e7\u00f5es pela matriz de confus\u00e3o n\u00f3s podemos utilizar a acur\u00e1cia, a precis\u00e3o o recall para definir qual modelo \u00e9 melhor para esse caso. S\u00e3o essas an\u00e1lises \u00e9 que v\u00e3o come\u00e7ar a dar forma ao seu projeto e voc\u00ea come\u00e7a a perceber certos padr\u00f5es na sua base de dados.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tecnologias de c\u00f3digo aberto s\u00e3o amplamente utilizadas em conjuntos de ferramentas de ci\u00eancia de dados. Quando hospedadas na nuvem, as equipes n\u00e3o precisam instalar, configurar, manter ou atualizar localmente. Um desafio cr\u00edtico \u00e9 a necessidade de profissionais qualificados que possam preencher a lacuna entre a Engenharia Mec\u00e2nica e a Ci\u00eancia de Dados\/IA. Os Engenheiros Mec\u00e2nicos [&hellip;]<\/p>\n","protected":false},"author":41,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-215","post","type-post","status-publish","format-standard","hentry","category-bootcamp-de-programacao-4"],"_links":{"self":[{"href":"https:\/\/drfarshadmohammadian.ir\/index.php\/wp-json\/wp\/v2\/posts\/215","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/drfarshadmohammadian.ir\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/drfarshadmohammadian.ir\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/drfarshadmohammadian.ir\/index.php\/wp-json\/wp\/v2\/users\/41"}],"replies":[{"embeddable":true,"href":"https:\/\/drfarshadmohammadian.ir\/index.php\/wp-json\/wp\/v2\/comments?post=215"}],"version-history":[{"count":1,"href":"https:\/\/drfarshadmohammadian.ir\/index.php\/wp-json\/wp\/v2\/posts\/215\/revisions"}],"predecessor-version":[{"id":216,"href":"https:\/\/drfarshadmohammadian.ir\/index.php\/wp-json\/wp\/v2\/posts\/215\/revisions\/216"}],"wp:attachment":[{"href":"https:\/\/drfarshadmohammadian.ir\/index.php\/wp-json\/wp\/v2\/media?parent=215"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/drfarshadmohammadian.ir\/index.php\/wp-json\/wp\/v2\/categories?post=215"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/drfarshadmohammadian.ir\/index.php\/wp-json\/wp\/v2\/tags?post=215"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}