Online or onsite, instructor-led live Reinforcement Learning training courses demonstrate through interactive hands-on practice how to create and deploy a Reinforcement Learning system.
Reinforcement Learning training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Plovdiv onsite live Reinforcement Learning trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Business Center Plovdiv
Han Kubrat St 1, Plovdiv, Bulgaria, 4017
This is the most modern business center in the city, with all the necessary functionalities, while being located in a green part of the city.
It is about 20 minutes by bus from the main train station as well as the city center.
This instructor-led, live training in Plovdiv (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 Plovdiv (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 Plovdiv (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.
Deep Reinforcement Learning (DRL) merges reinforcement learning principles with deep learning architectures, empowering agents to make decisions through interaction with their environments. This approach drives many modern AI innovations, including self-driving vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL enables artificial agents to learn strategies, optimize policies, and make autonomous decisions via trial and error using reward-based learning.
This instructor-led live training (available online or onsite) is designed for intermediate-level developers and data scientists who want to learn and apply Deep Reinforcement Learning techniques to build intelligent agents capable of autonomous decision-making in complex environments.
Upon completing this training, participants will be able to:
Grasp the theoretical foundations and mathematical principles of Reinforcement Learning.
Implement core RL algorithms, including Q-Learning, Policy Gradients, and Actor-Critic methods.
Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
Apply DRL to practical applications such as gaming, robotics, and decision optimization.
Troubleshoot, visualize, and optimize training performance using modern tools.
Format of the Course
Interactive lectures and guided discussions.
Hands-on exercises and practical implementations.
Live coding demonstrations and project-based applications.
Course Customization Options
To request a customized version of this course (e.g., using PyTorch instead of TensorFlow), please contact us to arrange.
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