Core algorithms, statistics, and training techniques
Hierarchical neural networks representations automatically
Layered architectures model relationships accurately
Techniques to process and understand natural language text
Algorithms interpreting and analyzing visual data effectively
Distributes traffic across multiple servers for reliability
Creating new data samples using learned distributions
Generates human-like text using massive pre-training data
Self-attention-based architecture powering modern AI models
Designing informative features significantly improving model performance
Learns useful representations without labeled data
Incorporates uncertainty using probabilistic model approaches
Crafting effective inputs to guide generative model outputs
Autonomous systems that perceive, decide, and act
Customizes pre-trained models for domain-specific tasks
Processes and generates across multiple data types
Transforms input into machine-readable vector formats
Finds similar items using dense vector embeddings
Assessing predictive performance using validation techniques
Deploying scalable systems to support AI operations