that we are confident will not be in the vocabulary. edl_sequence_output = vectorization_layer( [['efficient deep learning x123!']]).numpy()[0, :4] edl_sequence_output array([ 1, 1379, 1585, 1]) The code look up the indices in the vocabulary to make sure that it works well. ' '.join(np.take(vocabulary, edl_sequence_output)) '[UNK] deep learning [UNK]' Notice that the random token (x123!) and the word ‘efficient’

you to the book Deep Learning with Python1. All the code examples in this book are available at the EDL GitHub repository. The code examples for all the projects are available in the repository in the form

Cml::setApplicationDir([ 'secure_src' => CML_PROJECT_PATH . DIRECTORY_SEPARATOR . 'proj3e9840c31203003edl xa75b4', 'app_config_path_name' => 'Config',//自动载入配置用 'app_lang_path_name' => 'Lang',//自动载入语言用

15" description = "Open source message broker that implements the MQTT protocol" license = "EPL", "EDL" settings = "os", "arch", "compiler", "build_type" generators = "cmake" requires = "OpenSSL/1.0.2o@conan/stable"

15" description = "Open source message broker that implements the MQTT protocol" license = "EPL", "EDL" settings = "os", "arch", "compiler", "build_type" generators = "cmake" requires = "OpenSSL/1.0.2o@conan/stable"