1. AI Foundations
- Understanding neural network architectures (transformers, CNNs, RNNs)
- Model optimization techniques (hyperparameter tuning, regularization)
- Quantum machine learning fundamentals
2. Advanced Techniques
Fine-Tuning Large Language Models
Implement LoRA and QLoRA techniques for efficient model adaptation using HuggingFace and PyTorch.
Reinforcement Learning
Build decision-making systems with OpenAI Gym and Stable Baselines 3 for complex environments.
3. Ethical AI
Bias Mitigation
Implement fairness-aware algorithms using IBM AI Fairness 360
Explainability
Leverage SHAP and LIME for model interpretability
Regulatory Compliance
GDPR, AI Act, and HIPAA compliance frameworks for AI systems
4. Practical Implementation
from transformers import AutoModelForSequenceClassification
from peft import get_peft_model, LoraConfig
model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
config = LoraConfig(
r=8,
lora_alpha=16,
target_modules=["query", "value"],
lora_dropout=0.1
)
model = get_peft_model(model, config)
Example: Low-rank adaptation of BERT for efficient fine-tuning