š§ Core Advanced Topics
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
Reinforcement Learning (RL)
Policy gradients
Actor-critic methods (PPO, A3C)
Deep Q-networks (DQN)
Multi-agent RL
Applications in robotics and game theory
Natural Language Processing (NLP)
Large Language Models (LLMs)
Prompt engineering
Fine-tuning vs. pretraining
Retrieval-augmented generation (RAG)
Evaluation metrics for language models
Computer Vision
Object detection and segmentation (YOLO, Mask R-CNN)
Image generation and enhancement
Vision-language models (CLIP, DALLĀ·E)
āļø Practical & Technical Skills
Scalable ML/AI Systems
Model deployment (TensorFlow Serving, TorchServe)
Using cloud platforms (AWS SageMaker, GCP AI Platform)
Optimization and quantization (ONNX, TensorRT)
Experimentation and MLOps
CI/CD pipelines for ML
Model monitoring and versioning
Tools: MLflow, DVC, Weights & Biases
Data-centric AI
Data quality, bias, and augmentation
Active learning and data labeling strategies
š§Ŗ Research-Oriented Modules
AI Ethics and Safety
Bias, fairness, transparency
Explainable AI (XAI)
Alignment and value learning
Advanced Topics in Learning
Meta-learning
Few-shot and zero-shot learning
Causal inference in ML
Curriculum learning
Recent Research Papers
Reading and presenting top papers (from NeurIPS, ICML, CVPR, ACL, etc.)
Replicating experiments
Contributing to open-source or research