Concept

Word embedding

Related lectures (30)

Word Embeddings: Glove and Semantic Relationships

Explores word embeddings, Glove model, semantic relationships, subword embeddings, and syntactic relationships.

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

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Binary Sentiment Classifier Training

Covers the training of a binary sentiment classifier using an RNN.

Neural Word Embeddings: Learning Representations for Natural Language

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

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.

Deep Learning for NLP

Introduces deep learning concepts for NLP, covering word embeddings, RNNs, and Transformers, emphasizing self-attention and multi-headed attention.

Neural Word Embeddings

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

Deep Learning for NLP

Delves into Deep Learning for Natural Language Processing, exploring Neural Word Embeddings, Recurrent Neural Networks, and Attentive Neural Modeling with Transformers.

Embedding Models: Concepts and Retrieval

Covers embedding models for document retrieval, latent semantic indexing, SVD, and topic models.

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.

Natural Language Processing: Understanding Transformers and Tokenization

Provides an overview of Natural Language Processing, focusing on transformers, tokenization, and self-attention mechanisms for effective language analysis and synthesis.

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.

Modern NLP: A Simple Neural Classifier

Introduces the basics of modern NLP, covering word embeddings, neural models, and tasks like sequence labeling and text generation.

Optimization in Machine Learning

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

Pre-Training: BiLSTM and Transformer

Delves into pre-training BiLSTM and Transformer models for NLP tasks, showcasing their effectiveness and applications.

Word Embeddings: Pytorch Basics and Evaluation

Covers Pytorch basics, pre-trained word embeddings, evaluation methods, and practical exercises.

Text Models: Word Embeddings and Topic Models

Explores word embeddings, topic models, Word2vec, Bayesian Networks, and inference methods like Gibbs sampling.

Latent Semantic Indexing

Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.

Text Handling: Matrix, Documents, Topics

Explores text handling, focusing on matrices, documents, and topics, including challenges in document classification and advanced models like BERT.