Our interactive, instructor-led live Reinforcement Learning training sessions, available both online and on-site, provide practical, hands-on experience in building and deploying Reinforcement Learning systems.
Training options include "online live training" or "onsite live training." Online sessions, also referred to as "remote live training," utilize an interactive remote desktop environment. Onsite training can take place at your facilities in Lille or at NobleProg's corporate training centers in Lille.
NobleProg -- Your Local Training Provider
Lille, Gare Flandres
NobleProg Lille, 21 Avenue le Corbusier, Lille, france, 59800
In front of Flandres TGV Train Station
From Lille train stations
From Lille-Flandres: the address is essentially right next to the station — about 1–3 minutes on foot.
From Lille-Europe: walk toward Lille-Flandres along Avenue Le Corbusier. It is roughly 5–10 minutes on foot.
So you don't need a bus or metro if you're arriving by train.
If you're coming by bus
The nearest bus stops to Avenue Le Corbusier include Lion d'Or and Jacquet. Several local lines serve the area, including 13, 86, L5 and L91.
This instructor-led, live training in Lille (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 Lille (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 Lille (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.
This instructor-led training on Lille explores the theoretical underpinnings and practical application of Deep Reinforcement Learning using Python. Participants will design and train DRL agents using TensorFlow or PyTorch, leveraging algorithms like DQN and PPO to address complex, real-world challenges.
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