Knowledge Base
A comprehensive collection of notes on Artificial Intelligence, Machine Learning, and modern software engineering practices. This knowledge base is designed to serve both as a personal reference and an educational resource for practitioners at all levels.
Where to Start
New to AI and ML? Begin with 01 - AI Fundamentals to understand neural networks, activation functions, and optimization techniques. Then progress to 02 - Machine Learning for classical algorithms.
Interested in Large Language Models? Start with 05 - Generative AI to explore Transformers, prompt engineering, and RAG systems.
Building Production Systems? Head to 07 - MLOps & DevOps for pipelines, deployment strategies, and infrastructure as code.
Looking for Mathematical Foundations? See 08 - Algorithms & Math for linear algebra, probability, and algorithm design.
All Domains
| Domain | Description |
|---|---|
| 01 - AI Fundamentals | Neural networks, activation functions, loss functions, optimization |
| 02 - Machine Learning | Supervised and unsupervised learning, ensemble methods, feature engineering |
| 03 - Deep Learning | CNNs, RNNs, LSTMs, autoencoders, GANs |
| 04 - NLP | Text processing, word embeddings, semantic search |
| 05 - Generative AI | Transformers, LLMs, prompt engineering, RAG systems |
| 06 - Reinforcement Learning | MDPs, Q-learning, policy gradients, RLHF |
| 07 - MLOps & DevOps | ML pipelines, model serving, CI/CD, infrastructure as code |
| 08 - Algorithms & Math | Graph algorithms, clustering, linear algebra, probability theory |
Featured Articles
- Transformers - The architecture powering modern AI systems
- Attention Mechanisms - Understanding how models focus on relevant information
- Prompt Engineering - Techniques for effective interaction with LLMs
- Activation Functions - The building blocks of neural networks
- Model Evaluation - Metrics and techniques for assessing model performance
References
For a comprehensive introduction to deep learning and neural networks, see Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.1
Available at: https://www.deeplearningbook.org ↩︎