In Course 2 of the Natural Language Processing Specialization, you will: a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming, b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is vital for computational linguistics, c) Write a better auto-complete algorithm using an N-gram language model, and d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.
Word Embeddings
Skills You'll Learn
N-gram Language Models, Autocorrect, Parts-of-Speech Tagging, Word2vec
Reviews
4.7 (1,714 ratings)
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- 4 stars14.93%
- 3 stars3.26%
- 2 stars0.81%
- 1 star1.34%
SK
Jul 13, 2020
I have a wonderful experience. Try not to look at the hints, resolve yourself, it is excellent course for getting the in depth knowledge of how the black boxes work. Happy learning.
AH
Sep 28, 2020
Very good course! helped me clearly learn about Autocorrect, edit distance, Markov chains, n grams, perplexity, backoff, interpolation, word embeddings, CBOW. This was very helpful!
From the lesson
Word embeddings with neural networks
Learn about how word embeddings carry the semantic meaning of words, which makes them much more powerful for NLP tasks, then build your own Continuous bag-of-words model to create word embeddings from Shakespeare text.
Taught By
Younes Bensouda Mourri
Instructor
Łukasz Kaiser
Instructor