Training The basics of teaching with reinforcement
Practical information about training
- CATEGORY: Technologies
- SUBCATEGORY: AI
- TRAINING CODE: IT-AI-19
- DURATION: 3 days
- PRICE INFORMATION from: 3750 PLN net
- LANGUAGE OF TRAINING: polish
- FORM OF IMPLEMENTATION: stationary, online
Training description
The training introduces participants to the fascinating world of Reinforcement Learning, one of the most rapidly growing areas of artificial intelligence. The workshop program leads from fundamental concepts to advanced techniques for implementing agents that learn by interacting with the environment. Through a combination of theory and intensive hands-on exercises, participants learn to design, implement and optimize reinforcement learning systems in real-world applications.
Participant profile
- Data scientists interested in learning with reinforcement
- ML engineers developing decision-making systems
- AI developers implementing learning agents
- Researchers and scientists in the field of AI
- Process optimization specialists
- Developers of autonomous systems
Agenda
- Foundations of teaching with reinforcement
- Mathematical and theoretical foundations
- Markov decision processes
- Models of agents and environments
- Exploration and exploitation strategies
- Basic algorithms
- Q-learning and SARSA
- Policy Gradient Methods
- Temporal Difference Learning
- Monte Carlo Methods
- Advanced techniques
- Deep Reinforcement Learning
- Actor-Critic Architectures
- Multi-Agent Systems
- Transfer Learning
- Implementation and optimization
- Simulation environments
- Techniques for debugging agents
- Optimization of hyperparameters
- Implementation in production
Benefits
The participant will gain a thorough theoretical and practical knowledge of reinforcement learning and its applications. Will develop the ability to design and implement learning systems by interacting with the environment. Will master techniques for implementing and tuning algorithms for learning with reinforcement. Will learn to solve complex decision-making problems using RL methods. Will learn how to optimize and debug learning agents. Will be able to implement reinforcement learning systems in real-world applications.
Required preparation of participants
- Sound knowledge of mathematics and statistics
- Experience in Python programming
- Basic knowledge of machine learning
- Knowledge of the basics of deep learning
Issues
- Theory of learning with reinforcement
- Markov decision processes
- Q-learning algorithms
- Policy Gradient Methods
- Deep Reinforcement Learning
- Multi-agent systems
- Optimization of hyperparameters
- Simulation environments
- Transfer Learning
- Exploration and exploitation
- Debugging agents
- Production deployments
Do you have any questions?
Feel free to contact us.
Anna Polak
+48 600 010 440
anna.polak@eitt.academy
31 Ząbkowska Street 03-736 Warsaw
Forms of training delivery
Stationary training
- Training at the customer's premises or at a designated location
- Training room equipped with the necessary equipment
- Training materials in electronic form
- Coffee breaks and lunch
- Direct interaction with the trainer
- Networking in a group
- Workshop exercises in teams
Remote training
- Virtual training environment
- Electronic materials
- Interactive online exercises
- Breakout rooms for group work
- Technical support during the training
- Recordings of the session (optional)
Possibility of funding
The training can be financed with public funds under:
- National Training Fund (KFS)
- Development Services Base (BUR)
- EU projects implemented by PARP
- HR Academy Program (PARP)
- Regional operational programs
If you are interested in funding, our team will help you prepare the required documentation.
HAVE A QUESTION?
Contact us for more information about our training, programs and cooperation. We will be happy to answer all your inquiries!
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Do you have any questions?
Feel free to contact us.
Anna Polak
+48 600 010 440
anna.polak@eitt.academy
31 Ząbkowska Street 03-736 Warsaw
FAQ - Frequently Asked Questions
- One-pager invitation with deadlines
- Project kick-off
- Strategic leadership and thinking
- Communication and Cooperation. Conflict management
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- Best practices workshop - retrospective; creating a coherent program for middle and lower management levels























