Live instructor-led Reinforcement Learning courses, available online or onsite, utilize interactive hands-on exercises to demonstrate the creation and deployment of Reinforcement Learning systems.
Training options include “online live” or “onsite live” formats. Online live sessions, also known as “remote live training,” are conducted via an interactive remote desktop. Onsite live training can be delivered directly at customer premises in Varna or at NobleProg corporate training centers in Varna.
The "Central Point" complex offers quick access to main roads leading to the airport, the northern and southern resorts and the Varna - Sofia and Varna - Burgas highways.
This instructor-led, live training in Varna (online or on-site) targets intermediate-level data scientists aiming to develop a comprehensive understanding and practical skills in both Large Language Models (LLMs) and Reinforcement Learning (RL).
By the conclusion of this training, participants will be able to:
Understand the components and functionality of transformer models.
Optimize and fine-tune LLMs for specific tasks and applications.
Understand the core principles and methodologies of reinforcement learning.
Learn how reinforcement learning techniques can enhance the performance of LLMs.
This instructor-led, live training in Varna (online or onsite) is designed for advanced machine learning engineers and AI researchers who aim to apply RLHF to fine-tune large AI models for improved performance, safety, and alignment.
By the end of this training, participants will be able to:
Understand the theoretical foundations of RLHF and its importance in modern AI development.
Implement reward models based on human feedback to guide reinforcement learning processes.
Fine-tune large language models using RLHF techniques to align outputs with human preferences.
Apply best practices for scaling RLHF workflows for production-grade AI systems.
This instructor-led live training in Varna (online or onsite) is designed for advanced professionals looking to deepen their grasp of reinforcement learning and its practical applications in AI development using Google Colab.
By the end of the training, participants will be able to:
Understand the core concepts of reinforcement learning algorithms.
Implement reinforcement learning models using TensorFlow and OpenAI Gym.
Develop intelligent agents that learn through trial and error.
Optimize agents' performance using advanced techniques such as Q-learning and deep Q-networks (DQNs).
Train agents in simulated environments using OpenAI Gym.
Deploy reinforcement learning models for real-world applications.
This instructor-led training in Varna covers the theoretical foundations and practical implementation of Deep Reinforcement Learning using Python. Participants will build and train DRL agents with TensorFlow or PyTorch, applying key algorithms like DQN and PPO to solve complex real-world problems.
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