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Course Outline
Introduction to DeepSeek for AI Agents
- Overview of DeepSeek models and their applications in automation.
- Understanding AI agents and autonomous systems.
- Key challenges in AI-driven autonomy.
Integrating DeepSeek with AI Agents
- Using DeepSeek for decision-making and natural language processing.
- Connecting DeepSeek models to AI agent frameworks.
- Optimizing DeepSeek performance in autonomous systems.
Reinforcement Learning for Autonomous Systems
- Introduction to reinforcement learning concepts.
- Training AI agents with DeepSeek and reinforcement learning.
- Fine-tuning AI models for continuous learning.
Developing AI-Powered Robotics and Automation
- Using DeepSeek for robotics control and automation.
- Simulating AI-driven autonomy in OpenAI Gym and Gazebo.
- Deploying autonomous systems in real-world applications.
Ethical and Safety Considerations in AI Autonomy
- Ensuring ethical AI behavior in autonomous agents.
- Handling bias and fairness in AI-driven decision-making.
- Regulatory frameworks for autonomous AI systems.
Deploying and Scaling AI Agents
- Deploying AI agents on cloud platforms and edge devices.
- Scaling AI-driven automation for enterprise applications.
- Monitoring and maintaining autonomous AI systems.
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Understanding of machine learning concepts
- Familiarity with AI model deployment and optimization
Audience
- AI engineers
- Robotics developers
- Automation specialists
14 Hours
Testimonials (1)
Trainer responding to questions on the fly.
