Deep Machine Learning for Natural Language Processing

The course concentrates on the study of deep learning techniques and their use in natural language processing.

Topics: introduction to machine learning, regression, perceptron, neural networks, backpropagation, word vectors, word2vec and related models, dependency parsing, language modeling and RNNs, vanishing gradients and fancy RNNs, machine translation, seq2seq and attention, question answering, convolutional networks for NLP, contextual word embeddings, transformers, BERT, GPT-3 and related models, natural language generation, question answering for knowledge graphs, coreference resolution, dialogue systems and chatbots.

The programming exercises of the course are done using Python/TensorFlow/PyTorch.