| 001. About this course.mp4 | 12.6 MB | ||
| 001. About this course.srt | 3.2 KB | ||
| 002. Welcome video.mp4 | 20.1 MB | ||
| 002. Welcome video.srt | 7.3 KB | ||
| 003. Main approaches in NLP.mp4 | 30 MB | ||
| 003. Main approaches in NLP.srt | 9.6 KB | ||
| 004. Brief overview of the next weeks.mp4 | 26.2 MB | ||
| 004. Brief overview of the next weeks.srt | 9.5 KB | ||
| 005. [Optional] Linguistic knowledge in NLP.mp4 | 35 MB | ||
| 005. [Optional] Linguistic knowledge in NLP.srt | 12.7 KB | ||
| 006. Text preprocessing.mp4 | 51.3 MB | ||
| 006. Text preprocessing.srt | 20.2 KB | ||
| 007. Feature extraction from text.mp4 | 48.3 MB | ||
| 007. Feature extraction from text.srt | 18.3 KB | ||
| 008. Linear models for sentiment analysis.mp4 | 36.1 MB | ||
| 008. Linear models for sentiment analysis.srt | 12.6 KB | ||
| 009. Hashing trick in spam filtering.mp4 | 61.2 MB | ||
| 009. Hashing trick in spam filtering.srt | 22.9 KB | ||
| 010. Neural networks for words.mp4 | 50.7 MB | ||
| 010. Neural networks for words.srt | 19 KB | ||
| 011. Neural networks for characters.mp4 | 27.9 MB | ||
| 011. Neural networks for characters.srt | 10.4 KB | ||
| 012. Count! N-gram language models.mp4 | 33.9 MB | ||
| 012. Count! N-gram language models.srt | 13.5 KB | ||
| 013. Perplexity is our model surprised with a real text.mp4 | 26.8 MB | ||
| 013. Perplexity is our model surprised with a real text.srt | 10.4 KB | ||
| 014. Smoothing what if we see new n-grams.mp4 | 27.3 MB | ||
| 014. Smoothing what if we see new n-grams.srt | 9.3 KB | ||
| 015. Hidden Markov Models.mp4 | 49.4 MB | ||
| 015. Hidden Markov Models.srt | 16.6 KB | ||
| 016. Viterbi algorithm what are the most probable tags.mp4 | 39.3 MB | ||
| 016. Viterbi algorithm what are the most probable tags.srt | 13 KB | ||
| 017. MEMMs, CRFs and other sequential models for Named Entity Recognition.mp4 | 41.7 MB | ||
| 017. MEMMs, CRFs and other sequential models for Named Entity Recognition.srt | 14.5 KB | ||
| 018. Neural Language Models.mp4 | 31.5 MB | ||
| 018. Neural Language Models.srt | 11.8 KB | ||
| 019. Whether you need to predict a next word or a label - LSTM is here to help!.mp4 | 42.9 MB | ||
| 019. Whether you need to predict a next word or a label - LSTM is here to help!.srt | 14.9 KB | ||
| 020. Distributional semantics bee and honey vs. bee an bumblebee.mp4 | 28.3 MB | ||
| 020. Distributional semantics bee and honey vs. bee an bumblebee.srt | 11 KB | ||
| 021. Explicit and implicit matrix factorization.mp4 | 45.8 MB | ||
| 021. Explicit and implicit matrix factorization.srt | 15.4 KB | ||
| 022. Word2vec and doc2vec (and how to evaluate them).mp4 | 39.4 MB | ||
| 022. Word2vec and doc2vec (and how to evaluate them).srt | 12.7 KB | ||
| 023. Word analogies without magic king man + woman != queen.mp4 | 40.1 MB | ||
| 023. Word analogies without magic king man + woman != queen.srt | 12.8 KB | ||
| 024. Why words From character to sentence embeddings.mp4 | 42.8 MB | ||
| 024. Why words From character to sentence embeddings.srt | 14.6 KB | ||
| 025. Topic modeling a way to navigate through text collections.mp4 | 26 MB | ||
| 025. Topic modeling a way to navigate through text collections.srt | 8.9 KB | ||
| 026. How to train PLSA.mp4 | 23.5 MB | ||
| 026. How to train PLSA.srt | 8.6 KB | ||
| 027. The zoo of topic models.mp4 | 51.3 MB | ||
| 027. The zoo of topic models.srt | 16.9 KB | ||
| 028. Introduction to Machine Translation.mp4 | 57.1 MB | ||
| 028. Introduction to Machine Translation.srt | 18.8 KB | ||
| 029. Noisy channel said in English, received in French.mp4 | 21.7 MB | ||
| 029. Noisy channel said in English, received in French.srt | 7.6 KB | ||
| 030. Word Alignment Models.mp4 | 43.1 MB | ||
| 030. Word Alignment Models.srt | 15.4 KB | ||
| 031. Encoder-decoder architecture.mp4 | 22.4 MB | ||
| 031. Encoder-decoder architecture.srt | 8.1 KB | ||
| 032. Attention mechanism.mp4 | 31.2 MB | ||
| 032. Attention mechanism.srt | 12.1 KB | ||
| 033. How to deal with a vocabulary.mp4 | 40.1 MB | ||
| 033. How to deal with a vocabulary.srt | 14.5 KB | ||
| 034. How to implement a conversational chat-bot.mp4 | 38.2 MB | ||
| 034. How to implement a conversational chat-bot.srt | 14.2 KB | ||
| 035. Sequence to sequence learning one-size fits all.mp4 | 36.7 MB | ||
| 035. Sequence to sequence learning one-size fits all.srt | 13.4 KB | ||
| 036. Get to the point! Summarization with pointer-generator networks.mp4 | 41 MB | ||
| 036. Get to the point! Summarization with pointer-generator networks.srt | 15.3 KB | ||
| 037. Task-oriented dialog systems.mp4 | 42.3 MB | ||
| 037. Task-oriented dialog systems.srt | 17.1 KB | ||
| 038. Intent classifier and slot tagger (NLU).mp4 | 48 MB | ||
| 038. Intent classifier and slot tagger (NLU).srt | 18.5 KB | ||
| 039. Adding context to NLU.mp4 | 17.1 MB | ||
| 039. Adding context to NLU.srt | 6.9 KB | ||
| 040. Adding lexicon to NLU.mp4 | 28.4 MB | ||
| 040. Adding lexicon to NLU.srt | 10 KB | ||
| 041. State tracking in DM.mp4 | 44.9 MB | ||
| 041. State tracking in DM.srt | 17.5 KB | ||
| 042. Policy optimisation in DM.mp4 | 27.1 MB | ||
| 042. Policy optimisation in DM.srt | 10.1 KB | ||
| 043. Final remarks.mp4 | 21.6 MB | ||
| 043. Final remarks.srt | 7.4 KB | ||
| [FTU Forum].url | 204.8 B | ||
| [FreeCoursesOnline.Me].url | 102.4 B | ||
| [FreeTutorials.Us].url | 102.4 B | ||
| ▲ 89 total files | |||
[COURSERA] NATURAL LANGUAGE PROCESSING [FCO]
About this course: This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project is devoted to one of the most hot topics in today’s NLP. You will build your own conversational chat-bot that will assist with search on StackOverflow website. The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicates detection. Throughout the lectures, we will aim at finding a balance between traditional and deep learning techniques in NLP and cover them in parallel. For example, we will discuss word alignment models in machine translation and see how similar it is to attention mechanism in encoder-decoder neural networks. Core techniques are not treated as black boxes. On the contrary, you will get in-depth understanding of what’s happening inside. To succeed in that, we expect your familiarity with the basics of linear algebra and probability theory, machine learning setup, and deep neural networks. Some materials are based on one-month-old papers and introduce you to the very state-of-the-art in NLP research.
For more Coursera and other Courses >>> https://www.freecoursesonline.me/
For More Udemy Free Courses >>> http://www.freetutorials.us
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 861.5 MB | SunRiseZone | 2 years | 18 | 0 |
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