Aprendizagem Profunda (Dei)
pt
en
We will post here the slides of the theoretical lectures.
Full handout with all lectures in a single PDF in attachment.
Attachments
all_lectures_handout.pdf
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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
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