Available as either online or onsite, these instructor-led live Reinforcement Learning training courses focus on interactive, hands-on practice to demonstrate the creation and deployment of a Reinforcement Learning system.
This Reinforcement Learning training is offered as 'online live training' or 'onsite live training'. Online live training (also referred to as 'remote live training') is conducted via an interactive, remote desktop. Onsite live training can be delivered locally at the client's premises in Prague or at NobleProg corporate training centers in Prague.
From Prague Main Train Station (Praha hlavní nádraží)
Take tram 9 from Hlavní nádraží toward Sídliště Řepy.
Get off at Újezd.
Walk about 10 minutes toward Malostranské náměstí / Prokopská.
Continue along Prokopská to 296/8.
Alternative: Take the metro C from Hlavní nádraží → Muzeum, change to metro A → Malostranská, then walk across Malá Strana. This involves more walking.
From Prague Bus Station — Florenc
Take metro B from Florenc toward Zličín.
Get off at Můstek.
Change to metro A toward Nemocnice Motol.
Get off at Malostranská.
Walk approximately 10–15 minutes to Prokopská 296/8.
This instructor-led live training in Prague (online or onsite) is designed for 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 Prague (online or onsite) targets advanced-level machine learning engineers and AI researchers who wish to apply RLHF to fine-tune large AI models for superior performance, safety, and alignment.
Upon completion of this training, participants will be equipped to:
Grasp the theoretical underpinnings of RLHF and its critical role in contemporary AI development.
Construct reward models derived from human feedback to steer reinforcement learning processes.
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 systems.
This instructor-led, live training in Prague (online or on-site) is tailored for advanced professionals aiming to deepen their understanding of reinforcement learning and its practical applications in AI development using Google Colab.
Upon completion of this training, participants will be able to:
Grasp the fundamental concepts behind reinforcement learning algorithms.
Build reinforcement learning models using TensorFlow and OpenAI Gym.
Create intelligent agents that learn via trial and error.
Enhance agent performance through advanced techniques like Q-learning and deep Q-networks (DQNs).
Train agents within simulated environments provided by OpenAI Gym.
Deploy reinforcement learning models for real-world use cases.
This instructor-led training in Prague covers the theoretical foundations and practical implementation of Deep Reinforcement Learning using Python. Participants will build and train DRL agents with TensorFlow or PyTorch, applying key algorithms like DQN and PPO to solve complex real-world problems.
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