Representation Learning

Core concepts and self-supervised pretext tasks

1. 1. Distributed representations
2. 2. Autoencoders
3. 3. Contrastive learning basics
4. 4. Information theory in representation learning
5. 5. Self-supervised pretext tasks

CNNs, RNNs, transformers, and graph neural networks

1. 1. Convolutional neural networks
2. 2. Recurrent networks and sequence encoders
3. 3. Transformers and attention mechanisms
4. 4. Graph neural networks
5. 5. Normalizing flows

Contrastive learning and self-distillation techniques

1. 1. Instance discrimination and noise contrastive estimation
2. 2. Data augmentations and invariances
3. 3. Contrastive loss functions
4. 4. Masked modeling
5. 5. Self-distillation methods

VAEs, GANs, diffusion models, and disentanglement

1. 1. Variational autoencoders and ELBO
2. 2. Generative adversarial networks and training stability
3. 3. Diffusion models
4. 4. Energy-based models
5. 5. Disentangled representations

Vision-language models and cross-modal fusion

1. 1. Vision-language models
2. 2. Audio-text and speech representations
3. 3. Cross-modal fusion strategies
4. 4. Temporal transformers for video
5. 5. Multimodal evaluation

Scaling laws and training techniques

1. 1. Scaling laws
2. 2. Regularization techniques
3. 3. Curriculum learning and training pipelines
4. 4. Learning rate schedules
5. 5. Loss landscape and flat minima

Representation quality assessment and robustness

1. 1. Linear probing
2. 2. Diagnostic probing tasks
3. 3. Robustness testing
4. 4. Fairness and bias assessment
5. 5. Out-of-distribution detection

Distributed training and deployment optimization

1. 1. Distributed training
2. 2. Memory optimization
3. 3. Approximate nearest neighbor search
4. 4. Edge deployment
5. 5. Cost and carbon awareness

Search, retrieval, few-shot learning, and RLHF

1. 1. Search and retrieval systems
2. 2. Recommendation systems
3. 3. Few-shot and zero-shot learning
4. 4. Reinforcement learning from human feedback
5. 5. Domain-specific representations

Responsible AI and sustainability practices

1. 1. Data curation and licensing
2. 2. Privacy risks in representations
3. 3. Safety guardrails
4. 4. Model documentation and cards
5. 5. Sustainability in AI