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. Rome onsite live Reinforcement Learning trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Rome
NobleProg Rome, Via Clelia Bertini Attilj 4, Rome, italy, 34D
CoworkTalenti is located in the northern part of Rome, in the Talenti–Montesacro district, just a few minutes from the ring road. It is easily accessible via Via Nomentana.
This instructor-led, live training in Rome (online or onsite) targets intermediate-level data scientists who wish to gain a comprehensive understanding and practical skills in both Large Language Models (LLMs) and Reinforcement Learning (RL).
By the end 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 Rome (online or onsite) is designed for experienced machine learning engineers and AI researchers seeking to apply RLHF for fine-tuning large AI models to achieve superior performance, safety, and alignment.
Upon completion of this training, participants will be equipped to:
Grasp the theoretical underpinnings of RLHF and appreciate its critical role in contemporary AI development.
Develop reward models grounded in human feedback to steer reinforcement learning procedures.
Fine-tune large language models using RLHF methodologies to ensure outputs align with human preferences.
Apply industry best practices for scaling RLHF workflows within production-grade AI environments.
This instructor-led live training, located in Rome (available online or on-site), is designed for advanced professionals aiming to deepen their expertise in reinforcement learning and its application in AI development using Google Colab.
By the conclusion of this training, participants will be able to:
Comprehend the core concepts underlying reinforcement learning algorithms.
Build reinforcement learning models using TensorFlow and OpenAI Gym.
Construct intelligent agents that learn via trial and error.
Improve agent performance through advanced techniques including Q-learning and deep Q-networks (DQNs).
Conduct agent training within simulated environments using OpenAI Gym.
Deploy reinforcement learning models for tangible, real-world applications.
Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make informed decisions through interaction with their surroundings. This technology drives numerous contemporary AI innovations, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. By leveraging reward-based learning, DRL enables artificial agents to master strategies, refine policies, and execute autonomous decisions through trial and error.
This instructor-led live training, available both online and onsite, targets intermediate developers and data scientists eager to master and apply Deep Reinforcement Learning techniques. The course focuses on building intelligent agents capable of autonomous decision-making within complex environments.
Upon completion of this training, participants will be able to:
Grasp the theoretical foundations and mathematical underpinnings of Reinforcement Learning.
Implement core RL algorithms such as Q-Learning, Policy Gradients, and Actor-Critic methods.
Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
Deploy DRL solutions in real-world scenarios, including gaming, robotics, and decision optimization.
Utilize modern tools to troubleshoot, visualize, and optimize training performance.
Course Format
Interactive lectures paired with guided discussions.
Practical exercises and hands-on implementations.
Live coding demonstrations and project-based applications.
Customization Options
For customized versions of this course (e.g., utilizing PyTorch instead of TensorFlow), please reach out to us to arrange your specific requirements.
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