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Artificial Neural Networks and Deep Learning

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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

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