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Bibliografia
Principal
Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning:
James Stone
2019
Sebtel Press
Introduction to Deep Learning:
Eugene Charniak
2019
MIT Press
Deep Learning:
Ian Goodfellow, Yoshua Bengio, Aaron Courville
2016
MIT Press
Deep Learning with Python:
François Chollet
2017
Manning Publications
Machine Learning — A Journey to Deep Learning with Exercises and Answers:
Andreas Wichert and Luis Sa-Couto
2021
World Scientific, https://doi.org/10.1142/12201
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Componente de Programação e Computação
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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
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