Concept
Word2vec
Related lectures (65)
Word Embeddings: Glove and Semantic Relationships
Explores word embeddings, Glove model, semantic relationships, subword embeddings, and syntactic relationships.
Word Embedding Models: Optimization and Applications
Explores optimizing word embedding models, including loss function minimization and gradient descent, and introduces techniques like Fasttext and Byte Pair Encoding.
Link-based Ranking: Fundamentals and Algorithms
Covers the fundamentals and algorithms of link-based ranking, including anchor text indexing, PageRank, HITS, and practical implementations.
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.
Text Models: Word Embeddings and Topic Models
Explores word embeddings, topic models, Word2vec, Bayesian Networks, and inference methods like Gibbs sampling.
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.
Word Embeddings: Lab Session
Covers the implementation of a basic search engine using word embeddings and cosine similarity.
Latent semantic indexing: inverted files
Explores term-offset indices in inverted files and relevance feedback solutions.
Binary Sentiment Classifier Training
Covers the training of a binary sentiment classifier using an RNN.