- Machine Learning: Core algorithms, statistics, and model training techniques.
- Deep Learning: Hierarchical neural networks learning complex representations automatically.
- Neural Networks: Layered architectures efficiently model nonlinear relationships accurately.
- NLP: Techniques to process and understand natural language text.
- Computer Vision: Algorithms interpreting and analyzing visual data effectively
- Reinforcement Learning: Distributed traffic across multiple servers for reliability.
- Generative Models: Creating new data samples using learned data.
- LLM: Generates human-like text using massive pre-trained data.
- Transformers: Self-attention-based architecture powering modern AI models.
- Feature Engineering: Designing informative features to improve model performance significantly.
- Supervised Learning: Learns useful representations without labeled data.
- Bayesian Learning: Incorporate uncertainty using probabilistic model approaches.
- Prompt Engineering: Crafting effective inputs to guide generative model outputs.
- AI Agents: Autonomous systems that perceive, decide, and act.
- Fine-Tuning Models: Customizes pre-trained models for domain-specific tasks.
- Multimodal Models: Processes and generates across multiple data types like images, videos, and text.
- Embeddings: Transforms input into machine-readable vector formats.
- Vector Search: Finds similar items using dense vector embeddings.
- Model Evaluation: Assessing predictive performance using validation techniques.
- AI Infrastructure: Deploying scalable systems to support AI operations.
Are there any other AI concepts you would add to the list?
NoOn3 · 17h ago
”- Reinforcement Learning: Distributed traffic across multiple servers for reliability.“
I wonder how Reinforcement learning, Distributed traffic and multiple servers are related？:)
- Deep Learning: Hierarchical neural networks learning complex representations automatically.
- Neural Networks: Layered architectures efficiently model nonlinear relationships accurately.
- NLP: Techniques to process and understand natural language text.
- Computer Vision: Algorithms interpreting and analyzing visual data effectively
- Reinforcement Learning: Distributed traffic across multiple servers for reliability.
- Generative Models: Creating new data samples using learned data.
- LLM: Generates human-like text using massive pre-trained data.
- Transformers: Self-attention-based architecture powering modern AI models.
- Feature Engineering: Designing informative features to improve model performance significantly.
- Supervised Learning: Learns useful representations without labeled data.
- Bayesian Learning: Incorporate uncertainty using probabilistic model approaches.
- Prompt Engineering: Crafting effective inputs to guide generative model outputs.
- AI Agents: Autonomous systems that perceive, decide, and act.
- Fine-Tuning Models: Customizes pre-trained models for domain-specific tasks.
- Multimodal Models: Processes and generates across multiple data types like images, videos, and text.
- Embeddings: Transforms input into machine-readable vector formats.
- Vector Search: Finds similar items using dense vector embeddings.
- Model Evaluation: Assessing predictive performance using validation techniques.
- AI Infrastructure: Deploying scalable systems to support AI operations.
Are there any other AI concepts you would add to the list?
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