AI learning path
A practical route from AI foundations to AI security
Track local progress from AI concepts to machine learning workflow, evaluation, neural networks, implementation projects, traffic-defense notes, and AI security engineering.
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1
Concept map
AI Basics Learning Roadmap
Separate AI, machine learning, and deep learning before going into implementation details.
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2
Workflow
Machine Learning Workflow
Follow the practical path from data and features to training, prediction, and evaluation.
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3
Evaluation
Model Training and Evaluation
Understand loss, overfitting, train/test splits, accuracy, recall, and F1.
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4
Neural networks
Neural Network Basics
Move from perceptrons to activation, forward propagation, backpropagation, and training loops.
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5
Transformer
Transformer Self-Attention
Read Q/K/V, scaled dot-product attention, multi-head attention, and positional encoding before exploring LLM internals.
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6
LLM internals
LLM Visualizer
Inspect tokenization, embeddings, attention, next-token sampling, and KV cache with a local browser simulation.
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7
Practice
Python AI Mini Practice
Run a small scikit-learn classification task and read the experiment output.
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8
Security map
AI Security Threat Modeling
Use NIST, MITRE ATLAS, and OWASP to build a reviewable AI defense map.
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9
Robustness
Adversarial Examples and Robust Evaluation
Run an FGSM-style digits experiment and compare clean and perturbed accuracy.
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10
Data integrity
Data Poisoning and Backdoor Defense
Measure poison rate, trigger behavior, and attack success rate.
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11
Privacy
Model Privacy and Extraction Defense
Measure membership signal and surrogate fidelity for a local toy model.
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12
LLM security
LLM, RAG, and Agent Security
Separate instructions from data and enforce tool permission boundaries.