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Grand Hyatt Muscat

Way no. 3032, As Saruj St, Muscat 133, Muscat, Oman

Strategically located at Way no. 3032 on As Saruj Street in Muscat, Oman, this property sits at a key position within the capital city. As Saruj Street is a major thoroughfare, offering excellent access to other key areas of Muscat. The location provides convenient access to government offices, embassies, financial institutions, and vibrant commercial zones. Residents and businesses benefit from proximity to essential services, shopping centers, and dining options. This prime location makes it an ideal choice for both residential and commercial purposes, offering convenience, accessibility, and a desirable setting within the dynamic city of Muscat.

Explore Our Courses

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

 21 Hours

This instructor-led, live training in Oman (online or onsite) is aimed at intermediate-level data scientists who wish to gain 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.
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Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF)

 14 Hours

This instructor-led, live training in Oman (online or onsite) is aimed at 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.

By the end of this training, participants will be able to:

  • Understand the theoretical foundations of RLHF and why it is essential in modern AI development.
  • Implement reward models based on human feedback to guide reinforcement learning processes.
  • Fine-tune large language models using RLHF techniques to align outputs with human preferences.
  • Apply best practices for scaling RLHF workflows for production-grade AI systems.
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Reinforcement Learning with Google Colab

 28 Hours

This instructor-led, live training in Oman (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of reinforcement learning and its practical applications in AI development using Google Colab.

By the end of this training, participants will be able to:

  • Understand 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) combines reinforcement learning principles with deep learning architectures to enable agents to make decisions through interaction with their environments. It underpins many modern AI advancements such as self-driving vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL allows an artificial agent to learn strategies, optimize policies, and make autonomous decisions based on trial and error using reward-based learning.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers and data scientists who wish to learn and apply Deep Reinforcement Learning techniques to build intelligent agents capable of autonomous decision-making in complex environments.

By the end of this training, participants will be able to:

  • Understand the theoretical foundations and mathematical principles of Reinforcement Learning.
  • Implement key RL algorithms including Q-Learning, Policy Gradients, and Actor-Critic methods.
  • Build and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
  • Apply DRL to real-world applications such as games, robotics, and decision optimization.
  • Troubleshoot, visualize, and optimize training performance using modern tools.

Format of the Course

  • Interactive lecture and guided discussion.
  • Hands-on exercises and practical implementations.
  • Live coding demonstrations and project-based applications.

Course Customization Options

  • To request a customized version of this course (e.g., using PyTorch instead of TensorFlow), please contact us to arrange.
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Fundamentals of Reinforcement Learning

 21 Hours

This instructor-led, live training in Oman (online or onsite) is aimed at data scientists who wish to go beyond traditional machine learning approaches to teach a computer program to figure out things (solve problems) without the use of labeled data and big data sets.

By the end of this training, participants will be able to:

  • Install and apply the libraries and programming language needed to implement Reinforcement Learning.
  • Create a software agent that is capable of learning through feedback instead of through supervised learning.
  • Program an agent to solve problems where decision making is sequential and finite.
  • Apply knowledge to design software that can learn in a way similar to how humans learn.
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Upcoming Courses

Reinforcement Learning with Google Colab

2026-05-29 09:30
28 hours
Grand Hyatt Muscat

Fundamentals of Reinforcement Learning

2026-06-12 09:30
21 hours
Grand Hyatt Muscat

Deep Reinforcement Learning with Python

2026-06-26 09:30
21 hours
Grand Hyatt Muscat

Fundamentals of Reinforcement Learning

2026-07-10 09:30
21 hours
Grand Hyatt Muscat

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

2026-07-24 09:30
21 hours
Grand Hyatt Muscat

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