AI Fundamentals
The foundational concepts that underpin all of artificial intelligence and machine learning. These topics form the building blocks for understanding neural networks, optimization, and model development.1
Topics in This Section
| Topic | Description |
|---|---|
| Activation Functions | Non-linear functions that enable deep learning |
| Neural Networks | Architecture, layers, forward and backward propagation |
| Loss Functions | Measuring model error for optimization |
| Optimization | Gradient descent, optimizers (Adam, SGD, RMSprop) |
| Model Evaluation | Metrics, validation techniques, confusion matrices |
| Regularization | Preventing overfitting (L1, L2, Dropout) |
Learning Path
Neural Networks → Activation Functions → Loss Functions → Optimization → Regularization → Model Evaluation
Related Domains
- Ready for classical algorithms? See 02 - Machine Learning
- Want to explore neural architectures? See 03 - Deep Learning
- Interested in mathematical foundations? See 08 - Algorithms & Math
References
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Available at: https://www.deeplearningbook.org ↩︎