[FreeCoursesOnline.Me] Coursera - Natural Language Processing
File List
- 002.How to from plain texts to their classification/009. Hashing trick in spam filtering.mp4 61.2 MB
- 009.Statistical Machine Translation/028. Introduction to Machine Translation.mp4 57.1 MB
- 008.Topic models/027. The zoo of topic models.mp4 51.3 MB
- 002.How to from plain texts to their classification/006. Text preprocessing.mp4 51.3 MB
- 003.Simple deep learning for text classification/010. Neural networks for words.mp4 50.7 MB
- 005.Sequence tagging with probabilistic models/015. Hidden Markov Models.mp4 49.4 MB
- 002.How to from plain texts to their classification/007. Feature extraction from text.mp4 48.3 MB
- 012.Natural Language Understanding (NLU)/038. Intent classifier and slot tagger (NLU).mp4 48.0 MB
- 007.Word and sentence embeddings/021. Explicit and implicit matrix factorization.mp4 45.8 MB
- 013.Dialog Manager (DM)/041. State tracking in DM.mp4 44.9 MB
- 009.Statistical Machine Translation/030. Word Alignment Models.mp4 43.1 MB
- 006.Deep Learning for the same tasks/019. Whether you need to predict a next word or a label - LSTM is here to help!.mp4 42.9 MB
- 007.Word and sentence embeddings/024. Why words From character to sentence embeddings.mp4 42.8 MB
- 012.Natural Language Understanding (NLU)/037. Task-oriented dialog systems.mp4 42.3 MB
- 005.Sequence tagging with probabilistic models/017. MEMMs, CRFs and other sequential models for Named Entity Recognition.mp4 41.7 MB
- 011.Summarization and simplification tasks/036. Get to the point! Summarization with pointer-generator networks.mp4 41.0 MB
- 007.Word and sentence embeddings/023. Word analogies without magic king man + woman != queen.mp4 40.1 MB
- 010.Encoder-decoder-attention arhitecture/033. How to deal with a vocabulary.mp4 40.1 MB
- 007.Word and sentence embeddings/022. Word2vec and doc2vec (and how to evaluate them).mp4 39.4 MB
- 005.Sequence tagging with probabilistic models/016. Viterbi algorithm what are the most probable tags.mp4 39.3 MB
- 010.Encoder-decoder-attention arhitecture/034. How to implement a conversational chat-bot.mp4 38.2 MB
- 011.Summarization and simplification tasks/035. Sequence to sequence learning one-size fits all.mp4 36.7 MB
- 002.How to from plain texts to their classification/008. Linear models for sentiment analysis.mp4 36.1 MB
- 001.Introduction to NLP and our course/005. [Optional] Linguistic knowledge in NLP.mp4 35.0 MB
- 004.Language modeling it's all about counting!/012. Count! N-gram language models.mp4 33.9 MB
- 006.Deep Learning for the same tasks/018. Neural Language Models.mp4 31.5 MB
- 010.Encoder-decoder-attention arhitecture/032. Attention mechanism.mp4 31.2 MB
- 001.Introduction to NLP and our course/003. Main approaches in NLP.mp4 30.0 MB
- 012.Natural Language Understanding (NLU)/040. Adding lexicon to NLU.mp4 28.4 MB
- 007.Word and sentence embeddings/020. Distributional semantics bee and honey vs. bee an bumblebee.mp4 28.3 MB
- 003.Simple deep learning for text classification/011. Neural networks for characters.mp4 27.9 MB
- 004.Language modeling it's all about counting!/014. Smoothing what if we see new n-grams.mp4 27.3 MB
- 013.Dialog Manager (DM)/042. Policy optimisation in DM.mp4 27.1 MB
- 004.Language modeling it's all about counting!/013. Perplexity is our model surprised with a real text.mp4 26.8 MB
- 001.Introduction to NLP and our course/004. Brief overview of the next weeks.mp4 26.2 MB
- 008.Topic models/025. Topic modeling a way to navigate through text collections.mp4 26.0 MB
- 008.Topic models/026. How to train PLSA.mp4 23.5 MB
- 010.Encoder-decoder-attention arhitecture/031. Encoder-decoder architecture.mp4 22.4 MB
- 009.Statistical Machine Translation/029. Noisy channel said in English, received in French.mp4 21.7 MB
- 013.Dialog Manager (DM)/043. Final remarks.mp4 21.6 MB
- 001.Introduction to NLP and our course/002. Welcome video.mp4 20.1 MB
- 012.Natural Language Understanding (NLU)/039. Adding context to NLU.mp4 17.1 MB
- 001.Introduction to NLP and our course/001. About this course.mp4 12.6 MB
- 002.How to from plain texts to their classification/009. Hashing trick in spam filtering.srt 22.9 KB
- 002.How to from plain texts to their classification/006. Text preprocessing.srt 20.2 KB
- 003.Simple deep learning for text classification/010. Neural networks for words.srt 19.0 KB
- 009.Statistical Machine Translation/028. Introduction to Machine Translation.srt 18.8 KB
- 012.Natural Language Understanding (NLU)/038. Intent classifier and slot tagger (NLU).srt 18.5 KB
- 002.How to from plain texts to their classification/007. Feature extraction from text.srt 18.3 KB
- 013.Dialog Manager (DM)/041. State tracking in DM.srt 17.5 KB
- 012.Natural Language Understanding (NLU)/037. Task-oriented dialog systems.srt 17.1 KB
- 008.Topic models/027. The zoo of topic models.srt 16.9 KB
- 005.Sequence tagging with probabilistic models/015. Hidden Markov Models.srt 16.6 KB
- 009.Statistical Machine Translation/030. Word Alignment Models.srt 15.4 KB
- 007.Word and sentence embeddings/021. Explicit and implicit matrix factorization.srt 15.4 KB
- 011.Summarization and simplification tasks/036. Get to the point! Summarization with pointer-generator networks.srt 15.3 KB
- 006.Deep Learning for the same tasks/019. Whether you need to predict a next word or a label - LSTM is here to help!.srt 14.9 KB
- 007.Word and sentence embeddings/024. Why words From character to sentence embeddings.srt 14.6 KB
- 010.Encoder-decoder-attention arhitecture/033. How to deal with a vocabulary.srt 14.5 KB
- 005.Sequence tagging with probabilistic models/017. MEMMs, CRFs and other sequential models for Named Entity Recognition.srt 14.5 KB
- 010.Encoder-decoder-attention arhitecture/034. How to implement a conversational chat-bot.srt 14.2 KB
- 004.Language modeling it's all about counting!/012. Count! N-gram language models.srt 13.5 KB
- 011.Summarization and simplification tasks/035. Sequence to sequence learning one-size fits all.srt 13.4 KB
- 005.Sequence tagging with probabilistic models/016. Viterbi algorithm what are the most probable tags.srt 13.0 KB
- 007.Word and sentence embeddings/023. Word analogies without magic king man + woman != queen.srt 12.8 KB
- 001.Introduction to NLP and our course/005. [Optional] Linguistic knowledge in NLP.srt 12.7 KB
- 007.Word and sentence embeddings/022. Word2vec and doc2vec (and how to evaluate them).srt 12.7 KB
- 002.How to from plain texts to their classification/008. Linear models for sentiment analysis.srt 12.6 KB
- 010.Encoder-decoder-attention arhitecture/032. Attention mechanism.srt 12.1 KB
- 006.Deep Learning for the same tasks/018. Neural Language Models.srt 11.8 KB
- 007.Word and sentence embeddings/020. Distributional semantics bee and honey vs. bee an bumblebee.srt 11.0 KB
- 003.Simple deep learning for text classification/011. Neural networks for characters.srt 10.4 KB
- 004.Language modeling it's all about counting!/013. Perplexity is our model surprised with a real text.srt 10.4 KB
- 013.Dialog Manager (DM)/042. Policy optimisation in DM.srt 10.1 KB
- 012.Natural Language Understanding (NLU)/040. Adding lexicon to NLU.srt 10.0 KB
- 001.Introduction to NLP and our course/003. Main approaches in NLP.srt 9.6 KB
- 001.Introduction to NLP and our course/004. Brief overview of the next weeks.srt 9.5 KB
- 004.Language modeling it's all about counting!/014. Smoothing what if we see new n-grams.srt 9.3 KB
- 008.Topic models/025. Topic modeling a way to navigate through text collections.srt 8.9 KB
- 008.Topic models/026. How to train PLSA.srt 8.6 KB
- 010.Encoder-decoder-attention arhitecture/031. Encoder-decoder architecture.srt 8.1 KB
- 009.Statistical Machine Translation/029. Noisy channel said in English, received in French.srt 7.6 KB
- 013.Dialog Manager (DM)/043. Final remarks.srt 7.4 KB
- 001.Introduction to NLP and our course/002. Welcome video.srt 7.3 KB
- 012.Natural Language Understanding (NLU)/039. Adding context to NLU.srt 6.9 KB
- 001.Introduction to NLP and our course/001. About this course.srt 3.2 KB
- [FTU Forum].url 252 bytes
- [FreeCoursesOnline.Me].url 133 bytes
- [FreeTutorials.Us].url 119 bytes
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