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aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":items":{"title_v2":{"id":"title-v2-dc06b737a1","type":"heading2","lines":["Visão geral"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-154f5dbc0d","text":"\u003Cp\u003EO aprendizado profundo é um subconjunto de aprendizado de máquina que utiliza o poder das redes neurais artificiais para descobrir e modelar automaticamente os padrões complexos ocultos nos dados brutos. Ele se tornou o mecanismo que move os sistemas \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-programming-languages/\"\u003Emodernos de IA\u003C/a\u003E, provocando verdadeiras revoluções no reconhecimento de imagem e processamento de linguagem natural, e criando textos tão naturais e humanos que dão vida aos chatbots de IA. O aprendizado profundo também fornece a base para tecnologias autônomas, como veículos autônomos e robôs inteligentes, capazes de processar fluxos de informações de sensores em tempo real para entender o mundo ao redor e tomar decisões em frações de segundos.\u003C/p\u003E\n\u003Cp\u003EEste guia explicará o que é o aprendizado profundo e por que ele é importante, bem como discutirá suas vantagens e limitações.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"is",":items":{"title_v2":{"id":"title-v2-d29a2a41cc","additionalClasses":"headline-decoration","type":"heading2","lines":["O que é o aprendizado profundo?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-d419c136dc","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EO aprendizado profundo é um tipo avançado de \u003Ca href=\"https://www.snowflake.com/en/fundamentals/machine-learning-frameworks/\"\u003Eaprendizado de máquina\u003C/a\u003E que usa redes neurais multicamadas para aprender de modo automático padrões complexos diretamente dos \u003Ca href=\"https://www.snowflake.com/en/fundamentals/building-effective-machine-learning-pipelines/\"\u003Edados brutos\u003C/a\u003E. Diferente dos algoritmos tradicionais de aprendizado de máquina, o aprendizado profundo não precisa que as pessoas lhe digam que recursos devem ser observados, como bordas e cores em uma imagem ou padrões de palavras comuns em texto. Em vez disso, o aprendizado profundo depende de redes com muitas camadas de neurônios artificiais que descobrem automaticamente quais desses recursos são importantes. Esse processo autodidático requer conjuntos de dados de treinamento muito maiores para garantir que o \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ml-models/\"\u003Emodelo\u003C/a\u003E realmente entenda os padrões dos dados e não apenas os memorize. Além disso, como a maioria das redes neurais depende de dezenas de camadas de processamento diferentes, tudo feito simultaneamente, o aprendizado profundo também requer muito mais poder de processamento do que os algoritmos tradicionais de aprendizado de máquina.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_2060034519":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"work",":items":{"title_v2_copy":{"id":"title-v2-003bdbec09","additionalClasses":"headline-decoration","type":"heading2","lines":["Por que o aprendizado profundo é importante?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-f4509ac1f1","text":"\u003Cp\u003EA capacidade do aprendizado profundo de extrair automaticamente padrões significativos de dados não estruturados permite que as empresas automatizem tarefas antes impossíveis ou pouco práticas, como detecção de fraudes em tempo real, análise de imagens médicas e robótica de warehouse. As organizações que dominam o aprendizado profundo ganham a capacidade de processar dados não explorados, automatizar fluxos de trabalho complexos e identificar oportunidades de mercado mais rapidamente do que a concorrência. Isso torna a tecnologia indispensável para posicionamento estratégico de longo prazo em uma economia cada vez mais baseada em dados.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_289473020":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":items":{"container_copy_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":items":{"title_v2":{"id":"title-v2-2c99c35858","additionalClasses":"headline-decoration","type":"heading2","lines":["Exemplos e casos de uso de aprendizado profundo"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-b824654b03","text":"\u003Cp\u003EOs modelos de aprendizado profundo já estão em uso em diversos setores. Confira alguns exemplos:\u003C/p\u003E\r\n\u003Ch3\u003EDetecção de fraudes no setor bancário\u003C/h3\u003E\r\n\u003Cp\u003EOs sistemas de aprendizado profundo analisam os padrões de transações em tempo real para identificar atividades suspeitas que diferem do comportamento típico do cliente. Esses modelos podem sinalizar transações de alto risco para revisão ou bloqueá-las automaticamente, o que pode ajudar a reduzir as perdas de fraude e proteger os clientes de cobranças não autorizadas. \u003C/p\u003E\r\n\u003Ch3\u003EManutenção preditiva no setor industrial\u003C/h3\u003E\r\n\u003Cp\u003EO deep learning (aprendizado profundo) analisa os dados de sensores de máquinas industriais, tais como vibrações, temperatura e sinais acústicos, para identificar os sinais de alerta de uma falha iminente de equipamento. Com essa capacidade preditiva, os fabricantes podem agendar a manutenção durante o tempo de inatividade planejado, reduzindo drasticamente as paradas caras e prolongando a vida útil do equipamento e otimizando os custos de manutenção.\u003C/p\u003E\r\n\u003Ch3\u003ERecomendação personalizada no setor de varejo\u003C/h3\u003E\r\n\u003Cp\u003EAs plataformas de comércio eletrônico usam o aprendizado profundo para analisar o histórico de navegação, os padrões de compra e a similaridade com outros clientes, permitindo que elas recomendem outros produtos que possam interessar ao cliente. Ao mostrar sugestões mais personalizadas, o aprendizado profundo pode aumentar o engajamento dos clientes, podendo melhorar as taxas de conversão, dependendo da implementação e do contexto. \u003C/p\u003E\r\n\u003Ch3\u003EImagem e diagnóstico médico\u003C/h3\u003E\r\n\u003Cp\u003EModelos de aprendizado profundo treinados com milhões de imagens médicas, como raios X, tomografias computadorizadas, ressonâncias magnéticas (RMs) e fotografias de retinas, podem detectar doenças como câncer, doenças cardíacas e oftalmológicas. Esta tecnologia acelera o diagnóstico, reduz o erro humano e ajuda a diminuir a escassez global de especialistas médicos em regiões carentes. Em algumas tarefas e estudos limitados, os modelos de aprendizado profundo mostraram performance comparável a dos médicos. A eficácia real depende da validação, integração de fluxos de trabalho e supervisão clínica. \u003C/p\u003E\r\n\u003Ch3\u003EProcessamento de linguagem natural e chatbots\u003C/h3\u003E\r\n\u003Cp\u003EO aprendizado profundo possibilita o funcionamento de sistemas de IA conversacional que entendem a linguagem humana, permitindo que os chatbots forneçam suporte ao cliente, respondam a perguntas e completem transações sem intervenção humana. Ao aprenderem a partir de grandes volumes de dados de texto e conversacionais, esses bots são cada vez mais capazes de lidar com consultas complexas e de fornecer respostas naturais e úteis.\u003C/p\u003E\r\n\u003Ch3\u003EVeículos autônomos e robótica\u003C/h3\u003E\r\n\u003Cp\u003ERobôs e carros autônomos dependem do aprendizado profundo para processar feeds de câmeras, dados de LiDAR e fluxos de dados de sensores. Isso permite que eles entendam seu ambiente, detectem obstáculos e tomem decisões de navegação em tempo real. A habilidade de perceber o mundo ao redor permite que os sistemas autônomos se adaptem às variações nas condições de estrada, clima e comportamento humano.\u003C/p\u003E\r\n\u003Ch3\u003EReconhecimento de voz e processamento de áudio\u003C/h3\u003E\r\n\u003Cp\u003EOs modelos de aprendizado profundo convertem palavras faladas em texto com precisão extraordinária, potencializando assistentes de voz, como Siri e Alexa, bem como ferramentas de acessibilidade para pessoas com deficiência auditiva. Esses sistemas aprendem a lidar com diferentes sotaques, barulhos em segundo plano e padrões de fala, tornando a interação de voz uma interface prática em uma ampla variedade de dispositivos e serviços.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"and",":items":{"title_v2":{"id":"title-v2-9fad6e859c","additionalClasses":"headline-decoration","type":"heading2","lines":["Como o aprendizado profundo funciona?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-d60b37c98f","text":"\u003Cp\u003EModelos de aprendizado profundo são criados usando redes complexas compostas de milhares de neurônios artificiais, operações matemáticas que aprendem padrões de forma automática a partir de exemplos rotulados, ajustando milhões de configurações internas por meio de tentativa e erro até que eles possam prever com precisão ou reconhecer novos dados que nunca viram antes.\u003C/p\u003E\n\u003Cp\u003ECada rede é composta por três partes fundamentais: uma camada de entrada onde dados rotulados são ingeridos; várias camadas ocultas de neurônios que analisam os dados, refinando-os ainda mais a cada passagem; e uma camada de produção onde a previsão final é apresentada. \u003C/p\u003E\n\u003Cp\u003EDigamos que você queira treinar uma \u003Ca href=\"https://www.snowflake.com/pt_br/fundamentals/neural-network/\"\u003Erede neural\u003C/a\u003E para reconhecer se uma foto contém uma imagem de um cão ou um de gato. Você começa alimentando-a com milhares de imagens rotuladas como \"cão\" ou \"gato\", deixando a rede descobrir por conta própria as diferenças entre elas.\u003C/p\u003E\n\u003Cp\u003EAs primeiras camadas ocultas podem aprender a detectar padrões simples, como bordas e cantos. A segunda camada oculta combina essas bordas em formas, como círculos e linhas. Uma terceira camada pode reconhecer componentes, como \"orelhas pontudas\" ou \"nariz molhado\", e assim por diante. Com cada camada, a rede desenvolve uma compreensão mais sofisticada, passando de pixels brutos para conceitos significativos.\u003C/p\u003E\n\u003Cp\u003EA camada final contém a previsão da rede: uma pontuação de probabilidade indicando se ela acredita que a imagem está mostrando um canino ou um felino. Se a rede cometer um erro (ou seja, a previsão não corresponde à marcação dada aos dados originais), ela tentará novamente de forma automática, atribuindo mais peso a alguns recursos da imagem e menos peso a outros. Repita esse processo até poder distinguir corretamente um cão e um gato com alta precisão nos dados de testes realizados, dependendo da qualidade e da diversidade dos dados de treinamento e do design do modelo. \u003C/p\u003E\n\u003Cp\u003EUma rede neural aprende com seus erros por meio de um processo chamado retropropagação (ou backpropagation), voltando pelas camadas até identificar quais características mais contribuíram para a previsão incorreta. Uma fórmula matemática chamada função de perda de dados indica o quanto deve ser corrigido quando um erro ocorrer. Se um modelo se distanciar muito desta marca, ou seja, prever com 95% de confiança que uma foto de um gato é realmente um cão, ele examinará os recursos que levaram a previsão para a direção errada e aumentar ou diminuir o peso atribuído a eles. Se ele se distanciar um pouco (o modelo tem apenas 51% de certeza de que é uma foto de um cão) ele alterará os pesos de forma menos dramática.\u003C/p\u003E\n\u003Cp\u003EÉ por isso que o aprendizado profundo tornou-se tão eficaz: após definir esse processo de treinamento, ele descobre automaticamente os recursos e as representações úteis sem que o usuário precise desenvolvê-los manualmente. A rede aprende o que é importante. Além disso, à medida que você fornece mais dados e mais capacidade de processamento, a rede pode aprender padrões cada vez mais complexos, superando os limites do que a inteligência artificial pode realizar.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_259718203":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":items":{"title_v2":{"id":"title-v2-83cf4f32dd","additionalClasses":"headline-decoration","type":"heading2","lines":["Tipos de modelos de aprendizado profundo"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-ea24d8c8db","text":"\u003Cp\u003EExistem cerca de meia dúzia de arquiteturas diferentes de aprendizado profundo, cada uma direcionada a tipos específicos de dados e tarefas. Estas são as principais.\u003C/p\u003E\r\n\u003Ch3\u003ERedes neurais convolucionais (CNNs)\u003C/h3\u003E\r\n\u003Cp\u003EAs redes neurais convolucionais (convolutional neural networks, CNNs) foram especificamente desenvolvidas para processar dados semelhantes a redes, como imagens, buscando padrões, como bordas, texturas e formas. Como as CNNs entendem como os pixels próximos estão relacionados, elas se destacam em tarefas de visão computacional, como classificação de imagens, detecção de objetos, reconhecimento facial e análise de imagens médicas. Isso os torna altamente eficazes na criação de tudo, desde aplicações fotográficas para smartphone que identificam rostos até veículos autônomos que detectam pedestres e sinais de trânsito.\u003C/p\u003E\r\n\u003Ch3\u003ERedes neurais recorrentes (RNNs)\u003C/h3\u003E\r\n\u003Cp\u003EAs redes neurais recorrentes (recurrent neural networks, RNNs) são criadas para tarefas em que é importante manter a ordem na qual os dados aparecem, como analisar frases em um documento ou quadros em um vídeo. A capacidade de processar novos dados e, ao mesmo tempo, lembrar-se dos dados que foram analisados torna os RNNs úteis para tradução de linguagem, reconhecimento de fala e previsão de séries temporais. Embora as redes \u003Ci\u003Etransformers\u003C/i\u003E mais recentes as tenham substituído em grande parte para muitas tarefas de linguagem, as RNNs permanecem valiosas quando se trabalha com fluxos contínuos de dados, como leituras de sensores em tempo real, ou quando os recursos de processamento são limitados.\u003C/p\u003E\r\n\u003Ch3\u003ERedes adversárias generativas (GANs)\u003C/h3\u003E\r\n\u003Cp\u003EAs redes adversárias generativas (generative adversarial networks, GANs) são compostas por duas redes neurais que se enfrentam mutuamente: um gerador que cria dados sintéticos (como imagens falsificadas) e um discriminador que tenta distinguir dados reais e falsos. Através desse processo de treinamento adversário, o gerador torna-se cada vez mais hábil em produzir resultados realistas, tornando as GANs avançadas para criar imagens fotorrealistas, gerar dados de treinamento sintéticos e até mesmo produzir deepfakes. Foram usadas para criar trabalhos de arte, aprimorar imagens de baixa resolução, gerar rostos realistas de pessoas que não existem e ajudar a projetar novas moléculas para a descoberta de medicamentos.\u003C/p\u003E\r\n\u003Ch3\u003ERedes \u003Ci\u003ETransformer\u003C/i\u003E\u003C/h3\u003E\r\n\u003Cp\u003EOs \u003Ci\u003Etransformers\u003C/i\u003E revolucionaram o processamento de linguagem natural usando um "mecanismo de atenção" que permite que a rede se concentre nas partes mais relevantes da entrada de modo simultâneo, em vez de processar dados sequencialmente. Essa arquitetura viabiliza os modernos \u003Ca href=\"https://www.snowflake.com/pt_br/fundamentals/large-language-model/\"\u003Egrandes modelos de linguagem\u003C/a\u003E (LLMs), como o GPT e o Claude, permitindo que eles entendam o contexto em longas passagens de texto, gerem escrita semelhante ao ser humano e realizem tarefas como tradução e resumo com precisão sem precedentes. \u003Ci\u003ETransformers\u003C/i\u003E também se revelaram eficazes além da linguagem, com adaptações recentes demonstrando uma excelente performance na visão computacional e até mesmo na previsão da estrutura das proteínas.\u003C/p\u003E\r\n\u003Ch3\u003EAutoencoders\u003C/h3\u003E\r\n\u003Cp\u003EOs \u003Ci\u003Eautoencoders\u003C/i\u003E (autocodificadores) funcionam ao compactar os dados até seus recursos mais essenciais e, em seguida, reconstruí-los a partir desse formato compactado. Isso os torna úteis para detectar padrões incomuns (tudo o que não pode ser bem reconstruído é provavelmente anormal), limpar dados ruidosos e reduzir conjuntos de dados complexos para seus elementos centrais. A capacidade de detectar rapidamente anomalias nos dados torna os \u003Ci\u003Eautoencoders\u003C/i\u003E úteis para detectar transações fraudulentas de crédito ou detectar defeitos de produto nas linhas de montagem.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_289473020":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"equation",":items":{"title_v2_copy":{"id":"title-v2-fc9b013e2d","additionalClasses":"headline-decoration","type":"heading2","lines":["Principais diferenças entre aprendizado de máquina, aprendizado profundo e IA generativa"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-ab6ed2fed3","text":"\u003Cp\u003ETrês paradigmas de IA relacionados, mas distintos, dominam o desenvolvimento do modelo de IA hoje. Estas são as principais diferenças. \u003C/p\u003E\r\n\u003Ch3\u003EAprendizado de máquina\u003C/h3\u003E\r\n\u003Cp\u003EOs modelos de aprendizado de máquina usam algoritmos para aprender padrões com base nos dados. No entanto, eles geralmente exigem que as pessoas criem e extraiam manualmente recursos relevantes antes que o algoritmo possa aprender com eles. Esses sistemas funcionam bem com dados estruturados e tabulares e conjuntos de dados relativamente modestos, tornando-os práticos para aplicações como pontuação de crédito, segmentação de clientes e sistemas simples de recomendação. Em geral, os modelos de aprendizado de máquina são mais fáceis de interpretar do que os de aprendizado profundo e requerem menos capacidade de processamento para treinar e implementar.\u003C/p\u003E\r\n\u003Ch3\u003EAprendizado profundo\u003C/h3\u003E\r\n\u003Cp\u003EO aprendizado profundo usa redes neurais multicamadas que descobrem automaticamente quais recursos são importantes, eliminando a necessidade de engenharia de recursos manual que o aprendizado de máquina tradicional requer. Esses sistemas se destacam em dados não estruturados, como imagens, áudio e texto, mas exigem grandes conjuntos de dados de treinamento (geralmente milhões de exemplos) e recursos computacionais substanciais para aprender de forma eficaz. O aprendizado profundo alimenta as aplicações que requerem o entendimento de padrões complexos, como reconhecimento facial, veículos autônomos, diagnóstico de imagem médica e sistemas de reconhecimento de fala.\u003C/p\u003E\r\n\u003Ch3\u003EIA generativa\u003C/h3\u003E\r\n\u003Cp\u003EA \u003Ca href=\"https://www.snowflake.com/en/fundamentals/generative-ai/\"\u003EIA generativa\u003C/a\u003E é um subconjunto de aprendizado profundo. No entanto, ao invés de classificar ou prever os resultados dos dados existentes, ela foi especificamente projetada para criar novos conteúdos, incluindo texto, imagens, música, código ou vídeo. O treinamento desses sistemas requer conjuntos de dados realmente enormes (geralmente bilhões de exemplos) usando arquiteturas como transformadores e GANs que aprendem os padrões e as estruturas subjacentes dos dados de treinamento bem o suficiente para gerar resultados novos e realistas. A IA generativa é a base para aplicações como ChatGPT e Claude (IA conversacional), DALL-E e Midjourney (geração de imagem), GitHub Copilot (complementação de código) e sistemas que criam dados de treinamento sintéticos ou conteúdo personalizado em escala.\u003C/p\u003E\r\n\u003Cp\u003EAlém desses três, são importantes outros paradigmas de IA. A IA clássica (ou simbolística) usa regras, lógica e conhecimento explícitos programados por pessoas. Este é o paradigma usado por sistemas especializados e chatbots baseados em regras. No paradigma do aprendizado por reforço, os agentes de IA interagem com o ambiente e recebem recompensas ou penalidades, dependendo das ações tomadas. Esse modelo é muitas vezes implementado em sistemas de controle robótico e mecanismos de recomendação que aprendem com o engajamento do usuário. Os algoritmos evolucionários são inspirados pela evolução biológica, permitindo que os modelos se melhorem continuamente e se tornem mais adequados com o tempo. Eles são usados para resolver problemas, como design de rede neural ou otimização da cadeia de suprimentos. A IA neuro-simbólica combina redes neurais (aprendendo com base em dados) com raciocínio simbólico (regras e conhecimentos lógicos). Esse paradigma emergente está apenas começando a ver aplicações reais na melhoria dos diagnósticos médicos e na melhoria da segurança cibernética.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy__1985496925":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"why",":items":{"title_v2":{"id":"title-v2-15e162d121","additionalClasses":"headline-decoration","type":"heading2","lines":["Vantagens dos modelos de aprendizado profundo"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-05b8b090ae","text":"\u003Cp\u003EOs algoritmos de aprendizado profundo têm várias vantagens em relação a outros paradigmas de IA. Aqui estão alguns de seus maiores pontos fortes.\u003C/p\u003E\r\n\u003Ch3\u003ESão altamente precisos em tarefas complexas\u003C/h3\u003E\r\n\u003Cp\u003EO aprendizado profundo pode alcançar performance avançada em determinadas tarefas complexas (por exemplo,, classificação de imagens e reconhecimento de voz), dependendo do modelo, dos dados e da configuração de avaliação. Os modelos podem detectar características e relacionamentos sutis nos dados que seria quase impossível identificar ou programar explicitamente, como reconhecimento de sinais iniciais de doença em verificações médicas ou previsão de estruturas de proteínas. Essa vantagem em termos de precisão torna-se ainda mais pronunciada, à medida que as tarefas se tornam mais complexas, tornando o aprendizado profundo o método preferido para lidar com problemas que anteriormente tinham derrotado os métodos tradicionais.\u003C/p\u003E\r\n\u003Ch3\u003EIdentificam automaticamente recursos de dados relevantes \u003C/h3\u003E\r\n\u003Cp\u003EDiferente do aprendizado de máquina tradicional, o aprendizado profundo descobre automaticamente quais recursos são importantes sem exigir que especialistas de domínio os projetem e extraiam manualmente. A rede aprende a representar hierarquicamente por si só, identificando bordas nas camadas iniciais, combinando-as em formas nas camadas médias e reconhecendo conceitos de alto nível em camadas posteriores. Essa automação reduz drasticamente o tempo de desenvolvimento e permite que o aprendizado profundo resolva problemas em áreas em que especialistas humanos podem nem saber quais recursos são relevantes.\u003C/p\u003E\r\n\u003Ch3\u003EPossuem escala facilmente ajustável em grandes conjuntos de dados\u003C/h3\u003E\r\n\u003Cp\u003EOs modelos de aprendizado profundo melhoram de forma previsível à medida que você fornece mais dados de treinamento, enquanto os algoritmos tradicionais de aprendizado de máquina geralmente desaparecem após um certo ponto. Essa escalabilidade significa que as organizações com acesso a grandes conjuntos de dados podem obter performance bem melhor, investindo em mais coleta de dados e em modelos maiores. A relação entre volume e performance de dados cria uma vantagem crescente para as organizações capazes de coletar e processar informações em escala.\u003C/p\u003E\r\n\u003Ch3\u003EPodem tomar decisões em tempo real \u003C/h3\u003E\r\n\u003Cp\u003EApós serem treinados, os modelos de aprendizado profundo podem processar informações e fazer previsões com extrema rapidez, possibilitando aplicações em tempo real que requerem respostas instantâneas. Com essa velocidade, o aprendizado profundo é adequado para veículos autônomos que precisam detectar obstáculos e reagir imediatamente, sistemas de detecção de fraudes que avaliam transações conforme elas ocorrem e assistentes de voz que respondem a comandos falados sem atrasos notáveis. As otimizações modernas de hardware e as técnicas de compactação de modelos continuam a melhorar a velocidade de inferência, expandindo o alcance das aplicações em tempo real.\u003C/p\u003E\r\n\u003Ch3\u003ESão excelentes no tratamento de dados não estruturados\u003C/h3\u003E\r\n\u003Cp\u003EO aprendizado profundo se destaca no processamento de tipos de dados não estruturados que não têm uma organização tabular clara, como imagens, vídeo, áudio, texto e fluxos de sensores, com os quais os algoritmos tradicionais se esforçam. Esse recurso libera valor do enorme volume de emails, gravações de atendimento ao cliente, imagens de câmera de segurança e publicações de mídias sociais geradas pelas organizações. Ao tornar acessíveis para análise dados que antes não eram possíveis de serem aproveitados, o aprendizado profundo permite obter categorias totalmente novas de aplicações e insights.\u003C/p\u003E\r\n\u003Ch3\u003ESe adaptam rapidamente a novas tarefas\u003C/h3\u003E\r\n\u003Cp\u003EOs modelos de aprendizado profundo treinados em uma única tarefa podem ser adaptados a tarefas relacionadas com o mínimo de treinamento adicional, reduzindo drasticamente os dados e o tempo necessário para novas aplicações. Por exemplo, um modelo treinado para reconhecer objetos cotidianos pode ser ajustado para identificar condições médicas específicas, usando muito menos imagens médicas do que o treinamento do zero exigiria. Essa técnica, conhecida como aprendizado de transferência, permite que as organizações utilizem os modelos existentes como pontos de partida, acelerando os ciclos de desenvolvimento e tornando o aprendizado profundo mais acessível mesmo quando os dados específicos de domínio são limitados.\u003C/p\u003E\r\n\u003Ch3\u003EEstão sempre aprendendo \u003C/h3\u003E\r\n\u003Cp\u003EÉ possível atualizar continuamente os sistemas de aprendizado profundo com novos dados, permitindo que eles se adaptem aos padrões em constante mudança, melhorem a precisão com o tempo e lidem com condições em evolução sem retrabalho completo. Com esse recurso de aprendizado, os modelos implementados na produção podem se tornar melhores à medida que se encontram com exemplos mais reais, se ajustando naturalmente às mudanças nos comportamentos do usuário, condições de mercado ou fatores ambientais. A capacidade de melhorar de forma gradual torna os sistemas de aprendizado profundo mais sólidos e sustentáveis para implementação a longo prazo em comparação com sistemas estáticos baseados em regras.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"types",":items":{"title_v2_copy":{"id":"title-v2-dbb530f32d","additionalClasses":"headline-decoration","type":"heading2","lines":["Desvantagens dos modelos de aprendizado profundo"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-898b2d3cb0","text":"\u003Cp\u003EEmbora extremamente úteis em uma ampla variedade de aplicações, os modelos de aprendizado profundo também apresentam enormes desafios em termos de custo, consumo energético, interpretabilidade e potencial de uso indevido. Estas são as principais desvantagens do aprendizado profundo.\u003C/p\u003E\r\n\u003Ch3\u003EPrecisam de muito poder de computação \u003C/h3\u003E\r\n\u003Cp\u003ETreinar modelos de aprendizado profundo requer um poder de computação substancial, muitas vezes envolvendo hardware especializado caro, como GPUs, que permanecem em funcionamento por dias ou semanas. Seu consumo de energia pode ser enorme Treinar grandes modelos pode fazer uso intenso de energia e os requisitos variam muito de acordo com o tamanho do modelo, o hardware e a duração do treinamento. A implementação de modelos de inferência em tempo real em escala também requer recursos computacionais contínuos e investimento em infraestrutura, tornando o aprendizado profundo economicamente impraticável para algumas aplicações e organizações menores.\u003C/p\u003E\r\n\u003Ch3\u003EEles exigem grandes conjuntos de dados rotulados\u003C/h3\u003E\r\n\u003Cp\u003ENormalmente, os modelos de aprendizado profundo precisam de milhares ou milhões de exemplos de treinamento rotulados para funcionar bem, e a criação desses rótulos muitas vezes requer esforço e conhecimento humanos significativos. Em áreas especializadas, como a imagem médica ou o diagnóstico de doenças raras, onde especialistas precisam analisar e anotar manualmente cada exemplo, a obtenção de dados rotulados suficientes pode ser extremamente difícil ou cara. Esse requisito de dados cria um problema em que o aprendizado profundo não pode ser empregado de forma eficaz sem primeiro investir fortemente na coleta e na rotulagem dos dados, o que coloca aplicações avançadas fora do alcance de organizações sem grandes recursos de dados.\u003C/p\u003E\r\n\u003Ch3\u003EEles podem precisar de ajustes excessivos\u003C/h3\u003E\r\n\u003Cp\u003EOs modelos de aprendizado profundo podem acabar memorizando dados de treinamento em vez de aprender a identificar padrões dentro desses dados. Um modelo com sobreajustado apresenta uma performance excelente nos dados de treinamento, mas falha ao enfrentar situações novas e ligeiramente diferentes, tal como um sistema de reconhecimento facial que funciona perfeitamente em laboratório, mas tem dificuldades com diferentes condições de iluminação ou ângulos de câmera em produção. A prevenção do uso excedente exige técnicas como testes de regularização, cancelamento e validação. No entanto, mesmo com essas proteções, os modelos ainda podem aprender correlações improvisadas que não funcionam no mundo real.\u003C/p\u003E\r\n\u003Ch3\u003ETêm funcionamento obscuro\u003C/h3\u003E\r\n\u003Cp\u003ECom frequência, é impossível entender exatamente por que o modelo de aprendizado profundo fez uma determinada previsão, tornando-os problemáticos para aplicações em que as explicações são necessárias para fins jurídicos ou éticos. Por exemplo, um sistema de aprovação de empréstimos baseado em aprendizado profundo pode rejeitar um requerente sem ser capaz de explicar quais fatores conduziram essa decisão, potencialmente violando as leis de empréstimos justos ou perpetuando bias oculto. Esse "problema da caixa preta" traz desafios para setores regulamentados, como o setor de saúde e finanças, e dificulta depurar modelos quando eles falham ou verificar que eles estão tomando decisões pelas razões certas.\u003C/p\u003E\r\n\u003Ch3\u003ESuscitam sérias preocupações éticas\u003C/h3\u003E\r\n\u003Cp\u003EComo os modelos de aprendizado profundo aprendem com dados históricos, eles inevitavelmente absorvem e ampliam quaisquer bias existentes nesses dados, possivelmente perpetuando a discriminação em contratos, empréstimos, justiça criminal e outros domínios confidenciais. Um sistema de reconhecimento facial treinado principalmente em rostos com pele mais clara não terá boa performance em indivíduos com pele mais escura, e uma ferramenta de rastreio de resumo treinada com base em decisões históricas de contratação pode discriminar mulheres ou minorias. Além do bias, o aprendizado profundo traz várias preocupações éticas em relação à sua capacidade de gerar deepfakes, à sua função na possibilitação da vigilância de massa e ao seu uso com sistemas de armas autônomas.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"advantages",":items":{},":itemsOrder":[],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy__1190438074":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container_copy_2083166428":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","container_copy_20831":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer",":items":{"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"limitation",":items":{},":itemsOrder":[],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_2083166428":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"conslusion",":items":{"title_v2_copy":{"id":"title-v2-ff5e232ec5","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusão"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-2b8605237a","text":"\u003Cp\u003EO aprendizado profundo transformou fundamentalmente a inteligência artificial, permitindo que as máquinas aprendessem automaticamente padrões complexos a partir de dados brutos, desbloqueando recursos que eram impossíveis com abordagens tradicionais e possibilitando inovações em todos os setores, desde a saúde até sistemas autônomos. As organizações que dominam o aprendizado profundo ganham a capacidade de extrair valor de grandes quantidades de dados não estruturados, automatizar decisões sofisticadas em escala e identificar oportunidades que permanecem invisíveis para os concorrentes que dependem dos métodos convencionais. \u003C/p\u003E\n\u003Cp\u003EEssa tecnologia se tornou uma infraestrutura essencial para a economia moderna. Conforme os dados continuam a se proliferar e o poder computacional se torna mais acessível, a proficiência em aprendizado profundo separa cada vez mais os líderes e os seguidores do setor, tornando-o uma necessidade estratégica para qualquer organização que procure competir de forma eficaz em um futuro orientado por IA. A questão que as empresas enfrentam hoje não é mais se devem adotar o aprendizado profundo, mas a rapidez com que podem desenvolver os conhecimentos, a infraestrutura e os recursos de dados necessários para aproveitar seu potencial transformador.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_20831":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"faq",":items":{"title_v2_copy":{"id":"title-v2-92cb7c0b13","additionalClasses":"headline-decoration","type":"heading2","lines":["Perguntas frequentes sobre aprendizado profundo"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-60f45e923d","showDivider":false,"accordionItemsList":[{"title":"Qual a diferença entre o aprendizado profundo e a IA generativa?","richText":"\u003Cp\u003EO aprendizado profundo é uma abordagem ampla do aprendizado de máquina que usa redes neurais multicamadas para aprender padrões com base em dados. A IA generativa é um subconjunto específico do aprendizado profundo focado exclusivamente na criação de conteúdo novo, como texto, imagens, música, código ou vídeo. Utilizam redes neurais e processos de treinamento semelhantes. No entanto, eles são otimizados para objetivos completamente diferentes, como compreensão e criação.\u003C/p\u003E\n"},{"title":"Preciso ser um especialista em matemática ou saber programar para entender o aprendizado profundo?","richText":"\u003Cp\u003ENão é necessário ser um especialista em matemática para entender como as redes neurais aprendem com os dados. No entanto, se você realmente quiser criar e treinar modelos de aprendizado profundo você mesmo, será necessário ter habilidades de programação (geralmente Python) e pelo menos algum conhecimento de cálculo, álgebra linear e estatísticas para trabalhar de forma eficaz com as estruturas e os problemas de depuração.\u003C/p\u003E\n"},{"title":"O aprendizado profundo é realmente útil para lidar com problemas reais?","richText":"\u003Cp\u003EO aprendizado profundo está resolvendo problemas reais que antes eram impossíveis de resolver ou práticos, potencializando tudo, desde sistemas de diagnósticos médicos que detectam câncer até veículos autônomos. No entanto, não se trata de uma solução universal. A criação e a implementação de modelos de aprendizado profundo requerem dados substanciais, recursos computacionais e conhecimento especializado, o que torna o uso excessivo de problemas mais simples, onde os métodos tradicionais funcionam perfeitamente bem e custam muito menos.\u003C/p\u003E\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"},"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"customers",":items":{"title_v2":{"id":"title-v2-7782edc53b","additionalClasses":"headline-decoration","type":"heading2","lines":["Clientes que usam o Snowflake"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-4a1d268164","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-3a9424f7cd",":items":{"card_v2":{"id":"card-v2-d262811962","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003ECom a plataforma Snowflake como a base para a IA generativa, a Simon Data ajuda os profissionais de marketing a aumentar a receita oferecendo personalização contextual em escala. Tudo isso sem mover dados ou comprometer a governança.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical","title":{"id":"title","type":"heading4","lines":["Simon Data evolui o marketing com agentes modulares de IA desenvolvidos no Snowflake Cortex AI"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/simon-data/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Ler o caso"},"image":{"id":"image","alt":"merkle 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