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[FreeCoursesOnline.Me] Coursera - Natural Language Processing
TORRENT SUMMARY
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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.
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FILE LIST
Filename
Size
001.Introduction to NLP and our course/001. About this course.mp4
12.6 MB
001.Introduction to NLP and our course/001. About this course.srt
3.2 KB
001.Introduction to NLP and our course/002. Welcome video.mp4
20.1 MB
001.Introduction to NLP and our course/002. Welcome video.srt
7.3 KB
001.Introduction to NLP and our course/003. Main approaches in NLP.mp4
30 MB
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.mp4
26.2 MB
001.Introduction to NLP and our course/004. Brief overview of the next weeks.srt
9.5 KB
001.Introduction to NLP and our course/005. [Optional] Linguistic knowledge in NLP.mp4
35 MB
001.Introduction to NLP and our course/005. [Optional] Linguistic knowledge in NLP.srt
12.7 KB
002.How to from plain texts to their classification/006. Text preprocessing.mp4
51.3 MB
002.How to from plain texts to their classification/006. Text preprocessing.srt
20.2 KB
002.How to from plain texts to their classification/007. Feature extraction from text.mp4
48.3 MB
002.How to from plain texts to their classification/007. Feature extraction from text.srt
18.3 KB
002.How to from plain texts to their classification/008. Linear models for sentiment analysis.mp4
36.1 MB
002.How to from plain texts to their classification/008. Linear models for sentiment analysis.srt
12.6 KB
002.How to from plain texts to their classification/009. Hashing trick in spam filtering.mp4
61.2 MB
002.How to from plain texts to their classification/009. Hashing trick in spam filtering.srt
22.9 KB
003.Simple deep learning for text classification/010. Neural networks for words.mp4
50.7 MB
003.Simple deep learning for text classification/010. Neural networks for words.srt
19 KB
003.Simple deep learning for text classification/011. Neural networks for characters.mp4
27.9 MB
003.Simple deep learning for text classification/011. Neural networks for characters.srt
10.4 KB
004.Language modeling it's all about counting!/012. Count! N-gram language models.mp4
33.9 MB
004.Language modeling it's all about counting!/012. Count! N-gram language models.srt
13.5 KB
004.Language modeling it's all about counting!/013. Perplexity is our model surprised with a real text.mp4
26.8 MB
004.Language modeling it's all about counting!/013. Perplexity is our model surprised with a real text.srt
10.4 KB
004.Language modeling it's all about counting!/014. Smoothing what if we see new n-grams.mp4
27.3 MB
004.Language modeling it's all about counting!/014. Smoothing what if we see new n-grams.srt
9.3 KB
005.Sequence tagging with probabilistic models/015. Hidden Markov Models.mp4
49.4 MB
005.Sequence tagging with probabilistic models/015. Hidden Markov Models.srt
16.6 KB
005.Sequence tagging with probabilistic models/016. Viterbi algorithm what are the most probable tags.mp4
39.3 MB
005.Sequence tagging with probabilistic models/016. Viterbi algorithm what are the most probable tags.srt
13 KB
005.Sequence tagging with probabilistic models/017. MEMMs, CRFs and other sequential models for Named Entity Recognition.mp4
41.7 MB
005.Sequence tagging with probabilistic models/017. MEMMs, CRFs and other sequential models for Named Entity Recognition.srt
14.5 KB
006.Deep Learning for the same tasks/018. Neural Language Models.mp4
31.5 MB
006.Deep Learning for the same tasks/018. Neural Language Models.srt
11.8 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!.mp4
42.9 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!.srt
14.9 KB
007.Word and sentence embeddings/020. Distributional semantics bee and honey vs. bee an bumblebee.mp4
28.3 MB
007.Word and sentence embeddings/020. Distributional semantics bee and honey vs. bee an bumblebee.srt
11 KB
007.Word and sentence embeddings/021. Explicit and implicit matrix factorization.mp4
45.8 MB
007.Word and sentence embeddings/021. Explicit and implicit matrix factorization.srt
15.4 KB
007.Word and sentence embeddings/022. Word2vec and doc2vec (and how to evaluate them).mp4
39.4 MB
007.Word and sentence embeddings/022. Word2vec and doc2vec (and how to evaluate them).srt
12.7 KB
007.Word and sentence embeddings/023. Word analogies without magic king man + woman != queen.mp4
40.1 MB
007.Word and sentence embeddings/023. Word analogies without magic king man + woman != queen.srt
12.8 KB
007.Word and sentence embeddings/024. Why words From character to sentence embeddings.mp4
42.8 MB
007.Word and sentence embeddings/024. Why words From character to sentence embeddings.srt
14.6 KB
008.Topic models/025. Topic modeling a way to navigate through text collections.mp4
26 MB
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.mp4
23.5 MB
008.Topic models/026. How to train PLSA.srt
8.6 KB
008.Topic models/027. The zoo of topic models.mp4
51.3 MB
008.Topic models/027. The zoo of topic models.srt
16.9 KB
009.Statistical Machine Translation/028. Introduction to Machine Translation.mp4
57.1 MB
009.Statistical Machine Translation/028. Introduction to Machine Translation.srt
18.8 KB
009.Statistical Machine Translation/029. Noisy channel said in English, received in French.mp4
21.7 MB
009.Statistical Machine Translation/029. Noisy channel said in English, received in French.srt
7.6 KB
009.Statistical Machine Translation/030. Word Alignment Models.mp4
43.1 MB
009.Statistical Machine Translation/030. Word Alignment Models.srt
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