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Artificial Neural Networks and Deep Learning
ANN-DL
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insper/ann-dl
Artificial Neural Networks and Deep Learning
insper/ann-dl
ANN-DL
ANN-DL
Table of contents
Versions
2026.2
2026.2
Overview
Classes
Classes
1. Concepts
2. Data
2. Data
Overview
Feature Types
Distributions
Train/Val/Test Split
Data Leakage
Data Quality
Class Imbalance
3. Preprocessing
4. Neural Networks
5. Perceptron
6. Multi-Layer Perceptron
7. Optimization
8. Regularization
9. Metrics and Evaluation
9. Metrics and Evaluation
9.1. Classification
9.2. Regression
9.3. Generative
9.4. LLM
10. Deep Learning
11. Convolutional
12. Attention Mechanisms
13. Transformers
14. Transfer Learning
15. LLMs
16. Generative Models
17. VAE
18. GAN
19. CLIP
20. Stable Diffusion
21. Flow-Matching
22. Diffusion Transformers
23. Autoregressive Generation
Exercises
Exercises
1. Data
2. Perceptron
3. MLP
4. Transformers & Attention
5. VAE
6. LLM Fine-Tuning
Projects
Projects
1. Classification
2. Regression
3. Generative
2025.2
2025.2
Overview
Classes
Classes
1. Concepts
2. Data
3. Preprocessing
4. Neural Networks
5. Perceptron
6. Multi-Layer Perceptron
7. Optimization
8. Regularization
9. Metrics and Evaluation
9. Metrics and Evaluation
9.1. Classification
9.2. Regression
9.3. Generative
10. Deep Learning
11. Convolutional
12. Generative Models
13. GAN
14. VAE
15. CLIP
16. Stable Diffusion
17. Flow-Matching
Exercises
Exercises
1. Data
2. Perceptron
3. MLP
4. VAE
Projects
Projects
1. Classification
2. Regression
3. Generative
Definitions
Definitions
Latent Space vs. Embedding
Stable Diffusion vs. Flow-Matching
References
Terms and Conditions
Table of contents
Versions
Artificial Neural Networks and Deep Learning
Versions
2026.2
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2025.2
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