š§ Core Advanced Topics
1. Deep Learning Architectures
* Transformers (e.g., GPT, BERT, Vision Transformers)
* Generative models (GANs, VAEs, Diffusion Models)
* Sequence models (LSTMs, GRUs, Attention mechanisms)
* Self-supervised learning
2. Reinforcement Learning (RL)
* Policy gradients
* Actor-critic methods (PPO, A3C)
* Deep Q-networks (DQN)
* Multi-agent RL
* Applications in robotics and game theory
3. Natural Language Processing (NLP)
* Large Language Models (LLMs)
* Prompt engineering
* Fine-tuning vs. pretraining
* Retrieval-augmented generation (RAG)
* Evaluation metrics for language models
4. Computer Vision
* Object detection and segmentation (YOLO, Mask R-CNN)
* Image generation and enhancement
* Vision-language models (CLIP, DALLĀ·E)
āļø Practical & Technical Skills
1. Scalable ML/AI Systems
* Model deployment (TensorFlow Serving, TorchServe)
* Using cloud platforms (AWS SageMaker, GCP AI Platform)
* Optimization and quantization (ONNX, TensorRT)
2. Experimentation and MLOps
* CI/CD pipelines for ML
* Model monitoring and versioning
* Tools: MLflow, DVC, Weights & Biases
3. Data-centric AI
* Data quality, bias, and augmentation
* Active learning and data labeling strategies
š§Ŗ Research-Oriented Modules
1. AI Ethics and Safety
* Bias, fairness, transparency
* Explainable AI (XAI)
* Alignment and value learning
2. Advanced Topics in Learning
* Meta-learning
* Few-shot and zero-shot learning
* Causal inference in ML
* Curriculum learning
3. Recent Research Papers
* Reading and presenting top papers (from NeurIPS, ICML, CVPR, ACL, etc.)
* Replicating experiments
* Contributing to open-source or research
š§© Capstone Project / Thesis
* Build and deploy a complete AI solution (could be research-heavy or application-focused)
* Present results, write a research-style report, or even publish a paper