My Digital Garden!

This space serves as a comprehensive knowledge base where I document my learning journey and insights into Artificial Intelligence, Machine Learning, and modern software engineering. Unlike a traditional blog with chronological posts, this is structured as a living library of interconnected notes and articles.

Here, you’ll find deep dives into technical concepts, practical implementation guides, and architectural patterns I’ve encountered in my work as an AI Engineer.

Knowledge Base Structure

Navigate through the topics using the sidebar or explore the main sections below:

Artificial Intelligence & ML

• 01 - AI Fundamentals

  Start here for the building blocks: Neural Networks, Optimization, and Model Evaluation.

• 02 - Machine Learning

  Classical algorithms, Supervised/Unsupervised learning, and Feature Engineering.

• 03 - Deep Learning

  Advanced architectures: CNNs, RNNs, Autoencoders, and GANs.

Natural Language Processing

• 04 - NLP

  Text processing, Embeddings (Word2Vec, GloVe), and Semantic Search.

• 05 - Generative AI

  The cutting edge: Transformers, LLMs, RAG, and Prompt Engineering.

Engineering & Operations

• 06 - Reinforcement Learning

  Agents, Environments, and Reward Systems.

• 07 - MLOps & DevOps

  Productionizing AI: Pipelines, Deployment, and Infrastructure as Code.

• 08 - Algorithms & Math

  The theoretical foundations: Graph Algorithms and Mathematical concepts.

Feel free to explore the folders in the sidebar to discover more specific notes within each category.