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Course Outline
Foundations of Deep-Think Mode
- Understanding Deep-Think architecture
- Depth vs breadth reasoning patterns
- Evaluating when Deep-Think is appropriate
Long-Context Reasoning
- Handling extended input sequences
- Maintaining coherence across long outputs
- Tracking dependencies and constraints
Iterative and Multi-Step Problem Solving
- Designing stepwise reasoning prompts
- Validating intermediate conclusions
- Building reasoning loops and refinements
Advanced Analytical Workflows
- Structuring complex research questions
- Data-driven reasoning pipelines
- Scenario modeling and forecasting
Deep-Think for High-Stakes Domains
- Risk-sensitive problem framing
- Evaluating critical decisions
- Ensuring consistency and traceability
Prompt Engineering for Deep-Think Optimization
- Constructing high-yield prompts
- Shaping the model’s internal reasoning path
- Managing ambiguity and uncertainty
Integrating Deep-Think into Applications
- Combining Deep-Think with multimodal inputs
- Embedding reasoning features into workflows
- Automation and system-level orchestration
Evaluation and Refinement Techniques
- Assessing reasoning quality and reliability
- Error analysis and correction patterns
- Continuous improvement of reasoning pipelines
Summary and Next Steps
Requirements
- An understanding of machine learning principles
- Experience with Python-based AI workflows
- Familiarity with API-driven model integration
Audience
- Researchers
- Data scientists
- AI strategists
14 Hours
Testimonials (1)
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