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Concept

Word2vec

Related lectures (30)

Neural Word Embeddings

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

Neural Word Embeddings: Learning Representations for Natural Language

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

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.

Binary Sentiment Classifier Training

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

Contrastive losses: Word2Vec and Skip-gram

Covers contrastive losses in Word2Vec and Skip-gram models, negative sampling, Noise Contrastive Estimation, and InfoNCE/CPC.

Text Handling: Matrix, Documents, Topics

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

Language Models: From Theory to Computation

Explores the mathematics of language models, covering architecture design, pre-training, and fine-tuning, emphasizing the importance of pre-training and fine-tuning for various 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.

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.

Document Retrieval and Classification

Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors like BERT.

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.

Text Processing: Large Digital Text Collections Analysis

Delves into the processing of large digital text collections, exploring hidden regularities, text reuse, and TF-IDF analysis.

Word Embeddings: Context and Representation

Explores word embeddings, emphasizing word-context relationships and low-dimensional representations.

Document Classification: Features and Models

Introduces document classification using features like words and metadata, and models such as k-Nearest-Neighbors and word embeddings.

Word Embeddings: Glove and Semantic Relationships Log in to Mediaspace to watch this video

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

Word Embeddings: Models and Learning Log in to Mediaspace to watch this video

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

Deep Learning for NLP Log in to Mediaspace to watch this video

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

Deep Learning for NLP Log in to Mediaspace to watch this video

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

Deep Learning for NLP Log in to Mediaspace to watch this video

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