These instructor-led live Reinforcement Learning courses, available either online or onsite, utilize interactive hands-on practices to demonstrate the creation and deployment of Reinforcement Learning systems.
Training options include both “online live training” and “onsite live training.” Online live training (also referred to as “remote live training”) is conducted via an interactive remote desktop environment. Onsite live training can be delivered locally at the client’s premises in Athens or at NobleProg’s corporate training centers in Athens.
Take Metro Line 3 (M3) from Airport toward Dimotiko Theatro.
Get off at Evangelismos.
The address is on Vasilissis Sofias Avenue, very close to the station. Evangelismos station is directly on the avenue.
Allow roughly 40–50 minutes total, depending on waiting/walking time.
From Athens Central Railway Station (Larissa Station)
Take the metro to Syntagma and change to Line 3 toward Doukissis Plakentias/Airport.
Get off at Evangelismos.
Walk to No. 46.
By bus
There are several buses running along Vasilissis Sofias Avenue. Nearby stops include Rigilis Street and Syntagma, while the Evangelismos area is served by multiple bus routes.
The metro is generally the simplest option, especially if you're arriving with luggage.
This instructor-led, live training in Athens (online or onsite) is designed for intermediate-level data scientists who wish to acquire 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 live, instructor-led training in Athens, conducted either online or on-site, is designed for advanced machine learning engineers and AI researchers seeking to apply RLHF to optimize large AI models for superior performance, safety, and alignment.
Upon completion, participants will be able to:
Comprehend the theoretical foundations of RLHF and its significance in modern AI development.
Create reward models based on human feedback to steer reinforcement learning processes.
Refine large language models using RLHF methods to align outputs with human preferences.
Apply best practices for scaling RLHF workflows in production-grade AI systems.
This instructor-led, live training in Athens (online or onsite) targets advanced-level professionals who aim 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 fundamental principles underpinning reinforcement learning algorithms.
Construct reinforcement learning models utilising TensorFlow and OpenAI Gym.
Create intelligent agents that acquire knowledge through trial and error.
Enhance agent performance by applying advanced methodologies such as Q-learning and deep Q-networks (DQNs).
Train agents within simulated environments provided by OpenAI Gym.
Deploy reinforcement learning models for real-world utilisation.
This instructor-led training on Athens delves into the theoretical basis and practical application of Deep Reinforcement Learning using Python. Learners will construct and train DRL agents with TensorFlow or PyTorch, leveraging algorithms such as DQN and PPO to address complex real-world challenges.
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