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种子名称： [FreeCoursesOnline.Me] Coursera - Natural Language Processing
文件类型： 视频
文件数目： 43个文件
文件大小： 1.51 GB
收录时间： 2020-6-20 10:57
已经下载： 3次
资源热度： 167
最近下载： 2024-7-6 12:55

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[FreeCoursesOnline.Me] Coursera - Natural Language Processing.torrent

001.Introduction to NLP and our course/001. About this course.mp412.59MB

001.Introduction to NLP and our course/002. Welcome video.mp420.05MB

001.Introduction to NLP and our course/003. Main approaches in NLP.mp430.05MB

001.Introduction to NLP and our course/004. Brief overview of the next weeks.mp426.15MB

001.Introduction to NLP and our course/005. [Optional] Linguistic knowledge in NLP.mp435.03MB

002.How to from plain texts to their classification/006. Text preprocessing.mp451.26MB

002.How to from plain texts to their classification/007. Feature extraction from text.mp448.3MB

002.How to from plain texts to their classification/008. Linear models for sentiment analysis.mp436.13MB

002.How to from plain texts to their classification/009. Hashing trick in spam filtering.mp461.22MB

003.Simple deep learning for text classification/010. Neural networks for words.mp450.67MB

003.Simple deep learning for text classification/011. Neural networks for characters.mp427.92MB

004.Language modeling it's all about counting!/012. Count! N-gram language models.mp433.9MB

004.Language modeling it's all about counting!/013. Perplexity is our model surprised with a real text.mp426.78MB

004.Language modeling it's all about counting!/014. Smoothing what if we see new n-grams.mp427.26MB

005.Sequence tagging with probabilistic models/015. Hidden Markov Models.mp449.4MB

005.Sequence tagging with probabilistic models/016. Viterbi algorithm what are the most probable tags.mp439.28MB

005.Sequence tagging with probabilistic models/017. MEMMs, CRFs and other sequential models for Named Entity Recognition.mp441.69MB

006.Deep Learning for the same tasks/018. Neural Language Models.mp431.48MB

006.Deep Learning for the same tasks/019. Whether you need to predict a next word or a label - LSTM is here to help!.mp442.93MB

007.Word and sentence embeddings/020. Distributional semantics bee and honey vs. bee an bumblebee.mp428.26MB

007.Word and sentence embeddings/021. Explicit and implicit matrix factorization.mp445.81MB

007.Word and sentence embeddings/022. Word2vec and doc2vec (and how to evaluate them).mp439.44MB

007.Word and sentence embeddings/023. Word analogies without magic king man + woman != queen.mp440.07MB

007.Word and sentence embeddings/024. Why words From character to sentence embeddings.mp442.76MB

008.Topic models/025. Topic modeling a way to navigate through text collections.mp425.97MB

008.Topic models/026. How to train PLSA.mp423.52MB

008.Topic models/027. The zoo of topic models.mp451.26MB

009.Statistical Machine Translation/028. Introduction to Machine Translation.mp457.14MB

009.Statistical Machine Translation/029. Noisy channel said in English, received in French.mp421.66MB

009.Statistical Machine Translation/030. Word Alignment Models.mp443.09MB

010.Encoder-decoder-attention arhitecture/031. Encoder-decoder architecture.mp422.4MB

010.Encoder-decoder-attention arhitecture/032. Attention mechanism.mp431.18MB

010.Encoder-decoder-attention arhitecture/033. How to deal with a vocabulary.mp440.07MB

010.Encoder-decoder-attention arhitecture/034. How to implement a conversational chat-bot.mp438.18MB

011.Summarization and simplification tasks/035. Sequence to sequence learning one-size fits all.mp436.74MB

011.Summarization and simplification tasks/036. Get to the point! Summarization with pointer-generator networks.mp441.02MB

012.Natural Language Understanding (NLU)/037. Task-oriented dialog systems.mp442.26MB

012.Natural Language Understanding (NLU)/038. Intent classifier and slot tagger (NLU).mp447.95MB

012.Natural Language Understanding (NLU)/039. Adding context to NLU.mp417.07MB

012.Natural Language Understanding (NLU)/040. Adding lexicon to NLU.mp428.37MB

013.Dialog Manager (DM)/041. State tracking in DM.mp444.94MB

013.Dialog Manager (DM)/042. Policy optimisation in DM.mp427.08MB

013.Dialog Manager (DM)/043. Final remarks.mp421.62MB