Aprendizagem Profunda
pt
en
Guest lecture by Prof. Chrysoula Zerva.
Attachments
lecture_14.pdf
Página Inicial
Grupos
Avaliação
Bibliografia
Horário
Métodos de Avaliação
Objectivos
Planeamento
Programa
Turnos
Anúncios
Sumários
Prérequisitos
Componente Laboratorial
Componente de Programação e Computação
Competências Transversais
Princípios Éticos
Notas
Resultado dos QUC
Horários de dúvidas
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
Lecture 1 - Introduction to Python and NumPy
Lecture 2 - Perceptron
Lecture 3 - Linear and Logistic Regression
Lecture 4 - Neural Networks and Backpropagation
Lecture 5 - Introduction to Pytorch
Lecture 6 - Q&A about HW1
Lecture 7 - PCA and Auto-Encoders
Lecture 8 - Convolutional Neural Networks
Lecture 9 - Recurrent Neural Networks
Lecture 10 - Attention Mechanisms
Lecture 11 - Q&A about HW2
Lecture 12: Word Embeddings and Large Pretrained Models
Homework Assignment #1
Homework Assignment #2
Remote Exam
Grades
Exam solutions
