Skip to content

Artificial Neural Networks and Deep Learning

Answer Key

🇺🇸 English en
🇧🇷 Português pt

Artificial Neural Networks and Deep Learning

ANN-DL
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
    
    Preprocessing
    Preprocessing
    
    Overview
    
    Scaling & Normalization
    
    Transforming Shape
    
    The Curse of Dimensionality
    
    Dimensionality Reduction
    
    Handouts
  - 3. Neural Networks
  - 4. Perceptron
  - 5. Multi-Layer Perceptron
  - 6. Optimization
    6. Optimization
    
    Overview
    
    Handouts
  - 7. Regularization
    7. Regularization
    
    Overview
    
    Handouts
  - 8. Metrics and Evaluation
    8. Metrics and Evaluation
    
    8.1. Classification
    
    8.2. Regression
    
    8.3. Generative
    
    8.4. LLM
    
    Handouts
  - 9. Deep Networks
  - 10. Convolutional Networks
  - 11. Attention Mechanisms
    11. Attention Mechanisms
    
    Overview
    
    Tokens
    
    Embeddings
    
    Query, Key, Value
    
    Masks and Heads
    
    The Cost of Attention
  - 12. Transformers
    12. Transformers
    
    Overview
    
    The Block
    
    Output
    
    Generation
    
    What Changed Since 2017
    
    Scaling
  - 13. Vision Transformers
  - 14. Transfer Learning
  - 15. Large Language Models
  - 16. Generative Models
  - 17. Variational Autoencoders
  - 18. GANs
  - 19. CLIP
  - 20. Stable Diffusion
  - 21. Flow Matching
  - 22. Diffusion Transformers
  - 23. Autoregressive Generation
- Exercises
  Exercises
  - 1. Data
  - 2. Perceptron
  - 3. MLP
  - 4. VAE
- Projects
  Projects
  - 1. EDA
  - 2. Classification or Regression
- Template ↗
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
Support
Support
- Study Cards
- Cheat Sheet
- Course Map
- Interviews
- Exams
  Exams
  - Final Exam: Example
  - Answer Key
Playground
Definitions
Definitions
- Latent Space vs. Embedding
  Latent Space vs. Embedding
- Stable Diffusion vs. Flow-Matching
References
Terms and Conditions

ANN-DL
Support
Exams

Final Exam: commented answer key

Solve it before reading

The answer key only helps if you solve the whole exam first, on paper and timed.

Download the answer key (PDF) Back to the exam

Made with Zensical