Keras Deep Learning para Python

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Keras Deep Learning para Python
Artificial Intelligence Machine Learning Posts Tecnologia da Informação 

Keras, inicialmente desenvolvido como parte de um projeto de pesquisa chamado ONEIROS, é uma API de rede neural de alto nível de código aberto, escrita em Python e capaz de rodar em TensorFlow, CNTK, Theano. Keras foi projetado para permitir experimentação rápida com deep neural networks.

A biblioteca de deep learning se concentra em ser mínima, modular e extensível, além de agrupar bibliotecas de computação numérica com eficiência, como Theano e TensorFlow. Ele permite que seus usuários definam e treinem modelos de redes neurais em apenas algumas linhas curtas de código.

A interface de programação do aplicativo Keras é muito familiar para quem trabalhou com a conhecida e aceita API de aprendizado Scikit. Keras suporta redes convolucionais e recorrentes, bem como combinações de ambas. Funciona sem grandes esforços de CPU e GPU.

Os recursos da sua biblioteca incluem inúmeras implementações de blocos de construção de redes neurais amplamente usadas, como camadas, objetivos, funções de ativação, otimizadores e também várias ferramentas para trabalhar com dados de imagem e texto mais rapidamente.

A biblioteca Keras segue as práticas recomendadas para reduzir a carga cognitiva e, portanto, oferece APIs consistentes e diretas, minimizando o número de ações do usuário necessárias para casos de uso diários e também fornecendo feedback claro e acionável no caso de um erro. O modelo pode ser entendido como um gráfico ou uma sequência de módulos independentes totalmente configuráveis ​​que podem ser conectados com as menores restrições possíveis.

Por exemplo, otimizadores, esquemas de inicialização, camadas neurais, esquemas de suavização, funções de custo, funções de ativação, são todos módulos independentes que podem ser combinados para criar novos modelos.

Novos módulos provam ser mais simples de adicionar novas classes e funções, e os módulos que já existem fornecem muitos exemplos. A capacidade de criar novos módulos facilmente permite ao usuário total expressividade, tornando a biblioteca adequada para pesquisas avançadas. Não há arquivo de configuração de modelo separado no formato declarativo. Os modelos foram descritos em Python, que é compacto, mais confortável para depurar e também permite facilidade de extensibilidade.

Portanto, podemos concluir dizendo que Keras é um deep learning para Python, através do qual se pode aprender a pré-processar dados, modelar, avaliar e otimizar redes neurais. A Deep Learning Library acaba sendo uma alternativa de alto nível ao TensorFlow e Theano. Portanto, esta biblioteca se concentra na estrutura do modelo, evitando a complexidade da programação numérica nas GPUs.

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