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. Onsite live Reinforcement Learning trainings in Lyon can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Lyon, Swisslife Tower
NobleProg Lyon, 10 Place Charles Béraudier, Lyon, france, 69000
Located 200 meters far from the train station TGV, Swisslife Tower is today the most representative building of this quarter of Lyon. The Business Center offers you a perfect location for your training.
Gares TGV
100meters from Gare TGV Part-Dieu , porte du Rhône Exit
Aéroport
30 minutes from Lyon Saint Exupéry (Satolas)
Rhône Express from Saint Exupéry airport (Terminus Gare part-Dieu)
This instructor-led, live training in Lyon (online or onsite) is tailored for intermediate-level data scientists seeking to develop a comprehensive understanding and practical expertise in both Large Language Models (LLMs) and Reinforcement Learning (RL).
Upon completion of this training, participants will be capable of:
Grasping the components and functionality of transformer models.
Optimizing and fine-tuning LLMs for specific tasks and applications.
Comprehending the core principles and methodologies of reinforcement learning.
Understanding how reinforcement learning techniques can enhance the performance of LLMs.
This instructor-led, live training offered Lyon (online or onsite) is tailored for advanced machine learning engineers and AI researchers looking to apply RLHF to fine-tune large AI models for improved performance, safety, and alignment.
Upon completing this training, participants will be equipped to:
Grasp the theoretical underpinnings of RLHF and its critical role in contemporary AI development.
Develop reward models driven by human feedback to steer reinforcement learning workflows.
Fine-tune large language models using RLHF methodologies to ensure their outputs align with human preferences.
Implement industry best practices for scaling RLHF processes within production-ready AI infrastructure.
This live, instructor-led training in Lyon (available online or in-person) targets advanced professionals eager to deepen their understanding of reinforcement learning and its practical applications in AI development using Google Colab.
By the conclusion of this training, participants will be able to:
Comprehend 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 concepts with deep learning models, empowering agents to make decisions by interacting with their surroundings. This technology drives many modern AI innovations, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to learn strategies, refine policies, and make autonomous choices through trial and error, guided by reward-based feedback.
This instructor-led training, available online or onsite, targets intermediate-level developers and data scientists eager to master and apply Deep Reinforcement Learning techniques. Participants will learn to build intelligent agents capable of making autonomous decisions in complex environments.
Upon completion of this training, participants will be able to:
Grasp the theoretical foundations and mathematical principles underlying 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 scenarios such as gaming, robotics, and decision optimization.
Troubleshoot, visualize, and enhance training performance using contemporary tools.
Format of the Course also allows for the evaluation of participants.
Interactive lectures accompanied by guided discussions.
Hands-on exercises and practical implementation tasks.
Live coding demonstrations and project-based applications.
Course Customization Options
To request a customized version of this course (for example, utilizing PyTorch instead of TensorFlow), please contact us to make arrangements.
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