[FreeCoursesOnline Me] Coursera Natural Language Processing Torrent Download Locations

[FreeCoursesOnline.Me] Coursera - Natural Language Processing
    001.Introduction to NLP and our course
          001. About this course.mp4 -

          001. About this course.srt -

          002. Welcome video.mp4 -

          002. Welcome video.srt -

          003. Main approaches in NLP.mp4 -

          003. Main approaches in NLP.srt -

          004. Brief overview of the next weeks.mp4 -

          004. Brief overview of the next weeks.srt -

          005. [Optional] Linguistic knowledge in NLP.mp4 -

          005. [Optional] Linguistic knowledge in NLP.srt -

    002.How to from plain texts to their classification
          006. Text preprocessing.mp4 -

          006. Text preprocessing.srt -

          007. Feature extraction from text.mp4 -

          007. Feature extraction from text.srt -

          008. Linear models for sentiment analysis.mp4 -

          008. Linear models for sentiment analysis.srt -

          009. Hashing trick in spam filtering.mp4 -

          009. Hashing trick in spam filtering.srt -

    003.Simple deep learning for text classification
          010. Neural networks for words.mp4 -

          010. Neural networks for words.srt -

          011. Neural networks for characters.mp4 -

          011. Neural networks for characters.srt -

    004.Language modeling it's all about counting!
          012. Count! N-gram language models.mp4 -

          012. Count! N-gram language models.srt -

          013. Perplexity is our model surprised with a real text.mp4 -

          013. Perplexity is our model surprised with a real text.srt -

          014. Smoothing what if we see new n-grams.mp4 -

          014. Smoothing what if we see new n-grams.srt -

    005.Sequence tagging with probabilistic models
          015. Hidden Markov Models.mp4 -

          015. Hidden Markov Models.srt -

          016. Viterbi algorithm what are the most probable tags.mp4 -

          016. Viterbi algorithm what are the most probable tags.srt -

          017. MEMMs, CRFs and other sequential models for Named Entity Recognition.mp4 -

          017. MEMMs, CRFs and other sequential models for Named Entity Recognition.srt -

    006.Deep Learning for the same tasks
          018. Neural Language Models.mp4 -

          018. Neural Language Models.srt -

          019. Whether you need to predict a next word or a label - LSTM is here to help!.mp4 -

          019. Whether you need to predict a next word or a label - LSTM is here to help!.srt -

    007.Word and sentence embeddings
          020. Distributional semantics bee and honey vs. bee an bumblebee.mp4 -

          020. Distributional semantics bee and honey vs. bee an bumblebee.srt -

          021. Explicit and implicit matrix factorization.mp4 -

          021. Explicit and implicit matrix factorization.srt -

          022. Word2vec and doc2vec (and how to evaluate them).mp4 -

          022. Word2vec and doc2vec (and how to evaluate them).srt -

          023. Word analogies without magic king man + woman != queen.mp4 -

          023. Word analogies without magic king man + woman != queen.srt -

          024. Why words From character to sentence embeddings.mp4 -

          024. Why words From character to sentence embeddings.srt -

    008.Topic models
          025. Topic modeling a way to navigate through text collections.mp4 -

          025. Topic modeling a way to navigate through text collections.srt -

          026. How to train PLSA.mp4 -

          026. How to train PLSA.srt -

          027. The zoo of topic models.mp4 -

          027. The zoo of topic models.srt -

    009.Statistical Machine Translation
          028. Introduction to Machine Translation.mp4 -

          028. Introduction to Machine Translation.srt -

          029. Noisy channel said in English, received in French.mp4 -

          029. Noisy channel said in English, received in French.srt -

          030. Word Alignment Models.mp4 -

          030. Word Alignment Models.srt -

    010.Encoder-decoder-attention arhitecture
          031. Encoder-decoder architecture.mp4 -

          031. Encoder-decoder architecture.srt -

          032. Attention mechanism.mp4 -

          032. Attention mechanism.srt -

          033. How to deal with a vocabulary.mp4 -

          033. How to deal with a vocabulary.srt -

          034. How to implement a conversational chat-bot.mp4 -

          034. How to implement a conversational chat-bot.srt -

    011.Summarization and simplification tasks
          035. Sequence to sequence learning one-size fits all.mp4 -

          035. Sequence to sequence learning one-size fits all.srt -

          036. Get to the point! Summarization with pointer-generator networks.mp4 -

          036. Get to the point! Summarization with pointer-generator networks.srt -

    012.Natural Language Understanding (NLU)
          037. Task-oriented dialog systems.mp4 -

          037. Task-oriented dialog systems.srt -

          038. Intent classifier and slot tagger (NLU).mp4 -

          038. Intent classifier and slot tagger (NLU).srt -

          039. Adding context to NLU.mp4 -

          039. Adding context to NLU.srt -

          040. Adding lexicon to NLU.mp4 -

          040. Adding lexicon to NLU.srt -

    013.Dialog Manager (DM)
          041. State tracking in DM.mp4 -

          041. State tracking in DM.srt -

          042. Policy optimisation in DM.mp4 -

          042. Policy optimisation in DM.srt -

          043. Final remarks.mp4 -

          043. Final remarks.srt -

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