Word Embeddings: Glove and Semantic Relationships

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Word Embeddings: Models and Applications

Explores word embeddings, models like CBOW and Skipgram, Fasttext, Glove, subword embeddings, and their applications in document search and classification.

Neural Word Embeddings: Learning Representations for Natural Language

Covers neural word embeddings and methods for learning word representations in natural language processing.

Neural Word Embeddings

Introduces neural word embeddings and dense vector representations for natural language processing.

Word Embeddings: Modeling Word Context and Similarity

Covers word embeddings, modeling word context and similarity in a low-dimensional space.

Deep Learning for NLP

Explores deep learning for NLP, covering word embeddings, context representations, learning techniques, and challenges like vanishing gradients and ethical considerations.

Handling Text: Document Retrieval, Classification, Sentiment Analysis

Explores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.

Word Embeddings: Models and Learning

Explores word embeddings, context importance, and learning algorithms for creating new representations.

Optimization in Machine Learning

Explores optimization techniques, word embeddings, and recommendation systems in machine learning.

Word Embeddings: Introduction and Applications

Introduces word embeddings, explaining how they capture word meanings based on context and their applications in natural language processing tasks.

Vector Space Semantics (and Information Retrieval)

Explores the Vector Space model, Bag of Words, tf-idf, cosine similarity, Okapi BM25, and Precision and Recall in Information Retrieval.