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001. About this course.mp4
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12.6 MB
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001. About this course.srt
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3.2 KB
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002. Welcome video.mp4
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20.1 MB
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002. Welcome video.srt
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7.3 KB
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003. Main approaches in NLP.mp4
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30 MB
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003. Main approaches in NLP.srt
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9.6 KB
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004. Brief overview of the next weeks.mp4
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26.2 MB
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004. Brief overview of the next weeks.srt
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9.5 KB
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005. [Optional] Linguistic knowledge in NLP.mp4
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35 MB
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005. [Optional] Linguistic knowledge in NLP.srt
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12.7 KB
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006. Text preprocessing.mp4
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51.3 MB
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006. Text preprocessing.srt
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20.2 KB
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007. Feature extraction from text.mp4
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48.3 MB
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007. Feature extraction from text.srt
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18.3 KB
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008. Linear models for sentiment analysis.mp4
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36.1 MB
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008. Linear models for sentiment analysis.srt
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12.6 KB
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009. Hashing trick in spam filtering.mp4
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61.2 MB
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009. Hashing trick in spam filtering.srt
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22.9 KB
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010. Neural networks for words.mp4
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50.7 MB
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010. Neural networks for words.srt
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19 KB
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011. Neural networks for characters.mp4
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27.9 MB
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011. Neural networks for characters.srt
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10.4 KB
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012. Count! N-gram language models.mp4
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33.9 MB
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012. Count! N-gram language models.srt
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13.5 KB
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013. Perplexity is our model surprised with a real text.mp4
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26.8 MB
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013. Perplexity is our model surprised with a real text.srt
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10.4 KB
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014. Smoothing what if we see new n-grams.mp4
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27.3 MB
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014. Smoothing what if we see new n-grams.srt
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9.3 KB
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015. Hidden Markov Models.mp4
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49.4 MB
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015. Hidden Markov Models.srt
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16.6 KB
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016. Viterbi algorithm what are the most probable tags.mp4
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39.3 MB
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016. Viterbi algorithm what are the most probable tags.srt
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13 KB
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017. MEMMs, CRFs and other sequential models for Named Entity Recognition.mp4
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41.7 MB
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017. MEMMs, CRFs and other sequential models for Named Entity Recognition.srt
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14.5 KB
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018. Neural Language Models.mp4
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31.5 MB
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018. Neural Language Models.srt
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11.8 KB
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019. Whether you need to predict a next word or a label - LSTM is here to help!.mp4
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MP4
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42.9 MB
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019. Whether you need to predict a next word or a label - LSTM is here to help!.srt
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14.9 KB
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020. Distributional semantics bee and honey vs. bee an bumblebee.mp4
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28.3 MB
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020. Distributional semantics bee and honey vs. bee an bumblebee.srt
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11 KB
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021. Explicit and implicit matrix factorization.mp4
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45.8 MB
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021. Explicit and implicit matrix factorization.srt
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15.4 KB
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022. Word2vec and doc2vec (and how to evaluate them).mp4
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39.4 MB
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022. Word2vec and doc2vec (and how to evaluate them).srt
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12.7 KB
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023. Word analogies without magic king man + woman != queen.mp4
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40.1 MB
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023. Word analogies without magic king man + woman != queen.srt
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12.8 KB
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024. Why words From character to sentence embeddings.mp4
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42.8 MB
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024. Why words From character to sentence embeddings.srt
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14.6 KB
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025. Topic modeling a way to navigate through text collections.mp4
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MP4
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26 MB
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025. Topic modeling a way to navigate through text collections.srt
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8.9 KB
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026. How to train PLSA.mp4
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23.5 MB
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026. How to train PLSA.srt
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8.6 KB
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027. The zoo of topic models.mp4
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51.3 MB
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027. The zoo of topic models.srt
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16.9 KB
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028. Introduction to Machine Translation.mp4
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MP4
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57.1 MB
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028. Introduction to Machine Translation.srt
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SRT
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18.8 KB
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029. Noisy channel said in English, received in French.mp4
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21.7 MB
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029. Noisy channel said in English, received in French.srt
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7.6 KB
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030. Word Alignment Models.mp4
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MP4
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43.1 MB
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030. Word Alignment Models.srt
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15.4 KB
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031. Encoder-decoder architecture.mp4
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MP4
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22.4 MB
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031. Encoder-decoder architecture.srt
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8.1 KB
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032. Attention mechanism.mp4
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MP4
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31.2 MB
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032. Attention mechanism.srt
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SRT
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12.1 KB
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033. How to deal with a vocabulary.mp4
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MP4
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40.1 MB
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033. How to deal with a vocabulary.srt
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SRT
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14.5 KB
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034. How to implement a conversational chat-bot.mp4
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MP4
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38.2 MB
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034. How to implement a conversational chat-bot.srt
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SRT
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14.2 KB
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035. Sequence to sequence learning one-size fits all.mp4
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MP4
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36.7 MB
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035. Sequence to sequence learning one-size fits all.srt
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SRT
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13.4 KB
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036. Get to the point! Summarization with pointer-generator networks.mp4
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MP4
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41 MB
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036. Get to the point! Summarization with pointer-generator networks.srt
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15.3 KB
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037. Task-oriented dialog systems.mp4
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MP4
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42.3 MB
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037. Task-oriented dialog systems.srt
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17.1 KB
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038. Intent classifier and slot tagger (NLU).mp4
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MP4
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48 MB
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038. Intent classifier and slot tagger (NLU).srt
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18.5 KB
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039. Adding context to NLU.mp4
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MP4
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17.1 MB
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039. Adding context to NLU.srt
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SRT
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6.9 KB
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040. Adding lexicon to NLU.mp4
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MP4
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28.4 MB
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040. Adding lexicon to NLU.srt
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10 KB
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041. State tracking in DM.mp4
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MP4
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44.9 MB
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041. State tracking in DM.srt
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SRT
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17.5 KB
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042. Policy optimisation in DM.mp4
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MP4
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27.1 MB
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042. Policy optimisation in DM.srt
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SRT
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10.1 KB
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043. Final remarks.mp4
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MP4
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21.6 MB
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043. Final remarks.srt
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SRT
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7.4 KB
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[FTU Forum].url
|
URL
|
204.8 B
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[FreeCoursesOnline.Me].url
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URL
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102.4 B
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[FreeTutorials.Us].url
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URL
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102.4 B
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![[COURSERA] NATURAL LANGUAGE PROCESSING [FCO]](/static/img/no-torrent-image.png "[COURSERA] NATURAL LANGUAGE PROCESSING [FCO]")
