Self-Attention: Learned how tokens attend to other tokens within the same sequence, enabling parallel processing and long-range dependencies.
Multi-Head Attention: Understood how multiple attention heads capture different types of relationships simultaneously.
Scaled Dot-Product: Mastered the mathematical foundation enabling efficient attention computation.
Encoder-Decoder Architecture: Understanding of bidirectional encoding and autoregressive decoding.
Position Embeddings: How transformers handle sequence order without recurrence.
Layer Normalization: Stabilizing training in deep transformer networks.
Feed-Forward Networks: Point-wise transformations between attention layers.
Bidirectional Context: Unlike previous models, BERT considers both left and right context simultaneously.
Masked Language Modeling: Pre-training by predicting masked tokens in sentences.
Next Sentence Prediction: Understanding relationships between sentence pairs.
Transfer Learning: Fine-tuning pre-trained representations for downstream tasks.
GPT-1: Foundation of unsupervised pre-training for language understanding.
GPT-2: Scaling laws and the emergence of few-shot learning capabilities.
GPT-3/4: In-context learning, instruction following, and emergent abilities.
Code Generation: Programming assistance and code completion capabilities.
Full Fine-tuning: Updating all model parameters for maximum performance.
Parameter-Efficient Methods: LoRA, adapters, and prompt tuning for resource efficiency.
Task-Specific Adaptation: Customizing pre-trained models for specific domains.
Few-Shot Learning: Leveraging pre-trained knowledge with minimal data.
Natural Language Understanding (NLU): Intent recognition and entity extraction.
Dialog Management: Conversation flow control and context maintenance.
Natural Language Generation (NLG): Response generation and personalization.
Knowledge Integration: External knowledge bases and retrieval systems.
Data Pipeline: Text preprocessing, tokenization, and dataset preparation.
Model Architecture: Building transformer-based models from scratch.
Training Process: Loss functions, optimization, and convergence monitoring.
Evaluation Metrics: Accuracy, F1-score, BLEU, and domain-specific metrics.
Deployment: Model serving, API development, and production considerations.
Vision-Language Models: CLIP, DALL-E, and multimodal transformers that understand both images and text.
Cross-Modal Attention: How models align visual and textual representations.
Image Captioning: Generating natural language descriptions of visual content.
Visual Question Answering: Answering questions about image content.
Deep Q-Networks (DQN): Combining Q-learning with deep neural networks for complex state spaces.
Policy Gradient Methods: Direct optimization of policy functions using neural networks.
Actor-Critic Methods: Combining value-based and policy-based approaches.
Applications: Game playing, robotics, autonomous systems, and recommendation systems.
Diffusion Models: DALL-E 2, Stable Diffusion, and the mathematics of iterative denoising.
StyleGAN: Advanced GAN architectures for high-quality image synthesis.
Neural Style Transfer: Combining content and artistic style using deep networks.
Text-to-Image: Generating images from natural language descriptions.
Quantization: Reducing model precision for faster inference and smaller memory footprint.
Pruning: Removing unnecessary model parameters while maintaining performance.
Knowledge Distillation: Training smaller student models from larger teacher models.
Edge Deployment: Optimizing models for mobile and embedded devices.