| http://dbpedia.org/ontology/abstract
|
Seq2Seq（Sequence to sequence）模型，是将序列（Sequence）映射到序列的神經網絡機器學習模型。這個模型最初設計用於改進機器翻譯技術，可容許機器通過此模型發現及學習將一種語言的語句（詞語序列）映射到另一種語言的對應語句上。除此之外，Seq2Seq也能廣泛地應用到各種不同的技術上，如聊天機器人、Inbox by Gmail等，但需要有配對好的文本集才能訓練出對應的模型。 Seq2seq是用于自然语言处理的一系列机器学习方法。应用领域包括机器翻译，图像描述，对话模型和文本摘要。
, Seq2seq is a family of machine learning approaches used for natural language processing. Applications include language translation, image captioning, conversational models and text summarization.
|
| http://dbpedia.org/ontology/wikiPageExternalLink
|
https://medium.com/@devnag/seq2seq-the-clown-car-of-deep-learning-f88e1204dac3%7Ctitle=seq2seq: +
, https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html%7Ctitle=A +
, https://towardsdatascience.com/day-1-2-attention-seq2seq-models-65df3f49e263%7Ctitle=Attention +
|
| http://dbpedia.org/ontology/wikiPageID
|
62607005
|
| http://dbpedia.org/ontology/wikiPageLength
|
7394
|
| http://dbpedia.org/ontology/wikiPageRevisionID
|
1124279879
|
| http://dbpedia.org/ontology/wikiPageWikiLink
|
http://dbpedia.org/resource/Beam_Search +
, http://dbpedia.org/resource/Language_model +
, http://dbpedia.org/resource/GPT-2 +
, http://dbpedia.org/resource/Equation_solving +
, http://dbpedia.org/resource/Text_summarization +
, http://dbpedia.org/resource/Language_translation +
, http://dbpedia.org/resource/Symbolic_integration +
, http://dbpedia.org/resource/Category:Artificial_neural_networks +
, http://dbpedia.org/resource/Gated_recurrent_unit +
, http://dbpedia.org/resource/Recurrent_neural_network +
, http://dbpedia.org/resource/Pattern_recognition +
, http://dbpedia.org/resource/MATLAB +
, http://dbpedia.org/resource/Artificial_neural_network +
, http://dbpedia.org/resource/Amazon_%28company%29 +
, http://dbpedia.org/resource/Machine_learning +
, http://dbpedia.org/resource/Theano_%28software%29 +
, http://dbpedia.org/resource/Maple_%28software%29 +
, http://dbpedia.org/resource/Facebook +
, http://dbpedia.org/resource/Softmax_function +
, http://dbpedia.org/resource/Long_short-term_memory +
, http://dbpedia.org/resource/Sequence_transformation +
, http://dbpedia.org/resource/Natural_language_processing +
, http://dbpedia.org/resource/Conversational_model +
, http://dbpedia.org/resource/Parameter_%28machine_learning%29 +
, http://dbpedia.org/resource/Category:Google_software +
, http://dbpedia.org/resource/Machine_translation +
, http://dbpedia.org/resource/TensorFlow +
, http://dbpedia.org/resource/Loss_function +
, http://dbpedia.org/resource/OpenAI +
, http://dbpedia.org/resource/Image_captioning +
, http://dbpedia.org/resource/GPT-3 +
, http://dbpedia.org/resource/Bucket_%28computing%29 +
, http://dbpedia.org/resource/Wolfram_Mathematica +
, http://dbpedia.org/resource/Chatbot +
, http://dbpedia.org/resource/Vanishing_gradient_problem +
, http://dbpedia.org/resource/Attention_%28machine_learning%29 +
, http://dbpedia.org/resource/Torch_%28machine_learning%29 +
, http://dbpedia.org/resource/Noise_reduction +
, http://dbpedia.org/resource/Category:Natural_language_processing +
, http://dbpedia.org/resource/Differential_equation +
|
| http://dbpedia.org/property/wikiPageUsesTemplate
|
http://dbpedia.org/resource/Template:Reflist +
, http://dbpedia.org/resource/Template:Short_description +
, http://dbpedia.org/resource/Template:Cite_arXiv +
, http://dbpedia.org/resource/Template:Cite_web +
|
| http://purl.org/dc/terms/subject
|
http://dbpedia.org/resource/Category:Artificial_neural_networks +
, http://dbpedia.org/resource/Category:Google_software +
, http://dbpedia.org/resource/Category:Natural_language_processing +
|
| http://www.w3.org/ns/prov#wasDerivedFrom
|
http://en.wikipedia.org/wiki/Seq2seq?oldid=1124279879&ns=0 +
|
| http://xmlns.com/foaf/0.1/homepage
|
http://blog.keras.io +
|
| http://xmlns.com/foaf/0.1/isPrimaryTopicOf
|
http://en.wikipedia.org/wiki/Seq2seq +
|
| owl:sameAs |
http://zh.dbpedia.org/resource/Seq2Seq%E6%A8%A1%E5%9E%8B +
, http://www.wikidata.org/entity/Q41589189 +
, http://dbpedia.org/resource/Seq2seq +
, http://vi.dbpedia.org/resource/Seq2seq +
, https://global.dbpedia.org/id/3reik +
|
| rdfs:comment |
Seq2seq is a family of machine learning approaches used for natural language processing. Applications include language translation, image captioning, conversational models and text summarization.
, Seq2Seq（Sequence to sequence）模型，是将序列（Sequence）映射到序列的神經網絡機器學習模型。這個模型最初設計用於改進機器翻譯技術，可容許機器通過此模型發現及學習將一種語言的語句（詞語序列）映射到另一種語言的對應語句上。除此之外，Seq2Seq也能廣泛地應用到各種不同的技術上，如聊天機器人、Inbox by Gmail等，但需要有配對好的文本集才能訓練出對應的模型。 Seq2seq是用于自然语言处理的一系列机器学习方法。应用领域包括机器翻译，图像描述，对话模型和文本摘要。
|
| rdfs:label |
Seq2Seq模型
, Seq2seq
|
