Aprendizagem Profunda (Dei)
pt
en
We will post here the materials for the practical lectures.
Página Inicial
Grupos
Avaliação
Bibliografia
Horário
Métodos de Avaliação
Objectivos
Planeamento
Programa
Turnos
Anúncios
Sumários
Notas
Resultado dos QUC
Office hours
Theoretical Lectures
Lecture 1 - Introduction
Lecture 2 - Machine Learning Basics
Lecture 3 - Linear Models I
Lecture 4 - Linear Models II
Lecture 5 - Neural Networks I
Lecture 6 - Neural Networks II
Lecture 7 - Representation Learning
Lecture 8 - Convolutional Neural Networks
Lecture 9 - Recurrent Neural Networks
Lecture 10 - Sequence-to-Sequence Models
Lecture 11 - Attention Mechanisms and Transformers
Lecture 12 - Large Pretrained Models
Lecture 13 - Deep Generative Models
Lecture 14 - Interpretability and Fairness
Practical Lectures
Practical 1 - Introduction to Python and NumPy
Practical 2 - Perceptron
Practical 3 - Linear and Logistic Regression
Practical 4 - Neural Networks and Backpropagation
Practical 5 - Introduction to Pytorch
Practical 6 - Q&A about HW1
Practical 7 - PCA and Auto-Encoders
Practical 8 - Convolutional Neural Networks
Practical 9 - Recurrent Neural Networks
Practical 10 - Attention Mechanisms
Practical 11 - Q&A about HW2
Practical 12 - Word Embeddings and Large Pretrained Models
Homework Assignment #1
Homework Assignment #2
Remote Exam
Grades
Exam solutions
