Word Embeddings: Introduction and Applications

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

Neural Word Embeddings: Learning Representations for Natural Language

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

Natural Language Processing: A Primer

Introduces Natural Language Processing (NLP) and its applications, covering tokenization, machine learning, sentiment analysis, and Swiss NLP applications.

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.

Binary Sentiment Classifier Training

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

Deep Learning for NLP

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

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.

Word Embeddings: Modeling Word Context and Similarity

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

Neural Word Embeddings

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

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.