Lecture 10 (09/04/2024, 4h): notebooks on real-world reviewing example, training in NLP, hyperparameters, LSTMs

Notebooks on real-world review classification example, Part-of-Speech tagging brief introduction, LSTMs recap, Notebook on Part-of-Speech Tagging with LSTMs, data preprocessing and training procedure best practices.

 

Lecture 9 (04/05/2024, 2h): RNNs and Long-Short Term Memory Networks

Recurrent Neural Networks. Issues. Long-Short Term Memory Networks.

Lecture 8 (22/03/2024, 4h): introduction to language modeling

Negative sampling in the word2vec notebook. What is a language model? N-gram models (unigrams, bigrams, trigrams), together with their probability modeling and issues. Chain rule and n-gram estimation. Static vs. contextualized embeddings. Introduction to Recurrent Neural Networks.

Lecture 7 (21/03/2024, 2h): Word2vec notebook

PyTorch notebook on word2vec. More on homework 1.

Lecture 6 (15/03/2024, 4h): Negative sampling, homework 1 assignment

Negative sampling: the skipgram case; changes in the loss function. Homework 1 assignment.

Lecture 5 (14/03/2024, 2h): Word embeddings, word2vec

Word representations. Word embeddings. Word2vec (CBOW and skipgram), PyTorch notebook on word2vec.

Lecture 4 (08/03/2024, 4h): first hands-on with PyTorch with language detection

Recap of the Supervised Learning framework, hands on practice with PyTorch on the Language Detection Model: tensors, gradient tracking, the Dataset and DataLoader class, the Module class, the backward step, the training loop, evaluating a model.

Lecture 3 (07/03/2024, 2h): Supervised vs. unsupervised vs. reinforcement learning. PyTorch

Introduction to Supervised, Unsupervised & Reinforcement Learning. The Supervised Learning framework. From real to computational: features extraction and features vectors. Feature Engineering and inferred features. PyTorch. Introduction to Colab notebooks and first part of the PyTorch hands-on.

Lecture 2 (01/03/2024, 4h): Machine Learning for NLP and Logistic Regression

Basics of Machine Learning for NLP. Probabilistic classification. Logistic Regression and its use for classification. Explicit vs. implicit features. The cross-entropy loss function.

Lecture 1 (29/2/2024, 2h): Introduction

Introduction to the course. Introduction to Natural Language Processing: understanding and generation. What is NLP? The Turing Test, criticisms and alternatives. Tasks in NLP and its importance (with examples). Key areas and publication venues.