Learning Domains
Deep-dive into specific specialized branches of Artificial Intelligence, Machine Learning, and Systems Engineering.
AI Basics
Fundamental concepts of artificial intelligence and history.
Machine Learning
Supervised, unsupervised learning, regression, and classical models.
Deep Learning
Neural networks, backpropagation, CNNs, and optimization algorithms.
Transformers
Self-attention mechanisms, encoder-decoder models, and BERT/GPT internals.
LLMs
Large Language Model fine-tuning, quantization, scaling laws, and evaluation.
Agents
Autonomous reasoning loops, tool-calling architectures, and multi-agent systems.
Prompt Engineering
In-context learning, Chain-of-Thought, ReAct pattern, and system instructions.
Computer Vision
Image processing, object detection, segmentation, and diffusion models.
RAG
Retrieval-Augmented Generation, vector databases, embedding spaces, and hybrid search.
Deployment
Model serving, vLLM, Triton, serverless GPUs, and edge AI compilation.
Rust AI
High-performance ML engines, tokenizers, and systems development using Rust.
Cyber AI
AI red-teaming, prompt injection, automated hacking, and secure guardrails.