Teaching assistant of NLP

Teacher : Pierre Colombo

Introductory course to NLP, with practical sessions in Pytorch and HuggingFace.

Program

Courses:

• The Basics of NLP : This session introduces what is NLP, why it is challenging and how we approach any NLP problem.
• Representing text into vectors : This lecture covers representation learning techniques for Natural Language Processing.
• Deep Learning Methods for NLP : This lecture presents Deep Learning techniques used in NLP. We cover the design and training principles. We present the Multi-Layer-Perceptron (MLP), Recurrent Architectures (RNN and LSTM) and the transformer architecture.
• Language Modeling : This lecture introduces language modeling.
• Sequence Labeling & Classification : Sequence Labeling & Classification with Deep Learning models such as RNN-based models and transformers.
• Sequence Generation : Sequence Generation using Encoder-Decoders.

Labs :

• Introduction to textual data with Python : This lab introduces basics processing operations required for any NLP experiments. After introducing preprocessing tools for data cleaning and tokenization, we compute some descriptive statistics on textual data.
• Word Embeddings and their evaluation : This lab explores representation learning techniques for words and documents. It explores models like tf-idf and Word2vec and develop quantitative and qualtiative evaluation methods.
• Sequence Labeling and Sequence Classification with Deep Learning Models: This lab implements, trains and evaluates sequence classification and labeling models based on Recurrent Neural Networks and transformer deep-learning architecture.
• Machine Translation :This lab introduces basics processing operations required for any NLP experiments. After introducing preprocessing tools for data cleaning and tokenization, we compute some descriptive statistics on textual data.