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Nantes, Zenith

NobleProg Nantes, 4 rue Edith Piaf, Saint-Herblain, france, 44821

In the Parc d'Ar Mor zone, near the Zénith.
  • Car : from the ring road, Porte de Chézine Exit> Boulevard du Zenith > Esplanade Georges Brassens (restaurants) > Rue Edith Piaf on the right. From the N444 road (Nantes > Lorient), Exit #1 > boulevard Marcel Paul > Rue Edith Piaf at the right. Parking Zénith P1 (free). Once parked, you can recognize the building: it's one of the tree bulding with zinc frontage.
  • Bicycle: free indoor parking
  • Public transport :
    • Tramway R1, Schoelcher station + 10 mn by foot through commercial center Atlantis
    • Tramway R1, François Mitterrand stop + bus 50, stop at Saulzaie station or bus 71, stop at the Zénith station
    • Tramway R3, Marcel Paul station + bus 50, Saulzaie station
    • Chronobus C6, Hermeland station+ bus 71, Zénith station
    • Bus : lignes 50 (Saulzaie station) or 71 (Zénith station)

Explore Our Courses

Large Language Models (LLMs) and Reinforcement Learning (RL)

 21 Hours

This instructor-led, live training in Nantes (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.
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Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF)

 14 Hours

This instructor-led, live training offered Nantes (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.
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Reinforcement Learning with Google Colab

 28 Hours

This live, instructor-led training in Nantes (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.
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Deep Reinforcement Learning with Python

 21 Hours

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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Upcoming Courses

Reinforcement Learning with Google Colab

2026-07-27 09:30
28 hours
Nantes, Zenith
4950 EUR (Online)
5750 EUR (Classroom)

Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF)

2026-08-10 09:30
14 hours
Nantes, Zenith
2550 EUR (Online)
2950 EUR (Classroom)

Large Language Models (LLMs) and Reinforcement Learning (RL)

2026-08-24 09:30
21 hours
Nantes, Zenith
3750 EUR (Online)
4350 EUR (Classroom)

Reinforcement Learning with Google Colab

2026-09-07 09:30
28 hours
Nantes, Zenith
4950 EUR (Online)
5750 EUR (Classroom)

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