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Type: User
1.Create **EXACTLY**20 sentences. The maximum length of each sentence is FIVE (5). Two sample sentences may look like: “Excellent work”, “Good students work very hard”.2.Create word embeddings for the vocabulary (i.e. unique words ) in your sentences. You can choose one OR more word embedding methods from the embedding layer approach, the CBOW approach, the Skip-Gram approach, or the GloVe approach.Create a WORD document to answer the following questions for EACH of the word embedding method you choose: 1.Describe your embedding approach, architecture, and all the parameters (i.e. epochs, batches) you used. 2.Describe the dimensionality of your word vectors. 3.List the vocabulary in your training set. 4.Use the t-SNE method to reduce your word vectors to 2-dimension and p lot the 2-D points in a figure . Each point (vector) in the figure MUST be labeled with the original words in your vocabulary. (NOTE: you may need to adjust your training sentences so words with certain meaning cluster closer together in your t-SNE figure) 5.List the training time to train your word embedding model.
cocalc user manaual
A guide to document clustering in Python
Using Scikit-learn, machine learning library for the Python programming language.
Built text and image clustering models using unsupervised machine learning algorithms such as nearest neighbors, k means, LDA , and used techniques such as expectation maximization, locality sensitive hashing, and gibbs sampling in Python
Machine Learning - ppts and code
:memo: This repository recorded my NLP journey.
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