BESMALA / TECHNICAL NOTEBOOK
AI Learning Lab
A personal technical knowledge base: six connected batches that move from machine-learning foundations through model systems, practical engineering, and research reading. The supplied explanations, diagrams, examples, code, exercises, quizzes, and references remain available in context.
06 batches11 study sheets39 source SVG diagrams
Foundations of Machine Learning
Study the machine-learning workflow, data splits, generalization, feature selection, clustering, dimensionality reduction, and evaluation metrics.
- ML workflow
- Generalization
- Feature selection
- Evaluation
Deep Learning, CNNs, and Transformer Mechanics
Follow the supplied sheets from neurons, losses, gradients, and CNNs to tokens and the transformer learning process.
- Neural networks
- CNNs
- Transformers
- Optimization
Computer Vision and LLM Fundamentals
Explore computer-vision tasks and object detection, then the supplied introduction to large language models.
- Computer vision
- Object detection
- LLMs
RAG, Vector Databases, and AI Agents
Trace retrieval-augmented generation and vector databases, followed by agents and tool use.
- Retrieval
- Vector databases
- Agents
- Tool use
AI Engineering and Evaluation
Move from model development to production, then examine evaluation and responsible AI.
- Production systems
- Evaluation
- Responsible AI
Research Papers and Web Concepts for AI Engineers
Build a repeatable method for reading AI papers, then review the web concepts used by AI engineers.
- Research papers
- HTTP
- APIs
- Security
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