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The Torch tower

Aspire Park Rd, Doha, Qatar

Strategically located on Aspire Park Road in Doha, Qatar, The Torch stands as a prominent and iconic landmark. Designed by the renowned architectural firm Atkins, this distinctive structure is celebrated for its innovative design and striking resemblance to a torch, reaching a height of 220 meters (722 feet). Beyond its architectural significance, The Torch serves as a major hub for recreation, culture, and entertainment within Aspire Park. Its exterior is covered in photovoltaic panels, making it one of the most environmentally friendly buildings in the region, harnessing solar energy. Inside, it houses a variety of facilities, including exhibition spaces, a planetarium, a bowling alley, restaurants, and shops, offering diverse activities for visitors and residents alike. It is a key component of Doha's skyline and a popular destination for locals and tourists.

Explore Our Courses

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

 21 Hours

This instructor-led, live training in Qatar (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 Qatar (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 Qatar (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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Upcoming Courses

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

2026-08-19 09:30
21 hours
The Torch tower

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

2026-09-02 09:30
21 hours
The Torch tower

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

2026-09-16 09:30
14 hours
The Torch tower

Deep Reinforcement Learning with Python

2026-09-30 09:30
21 hours
The Torch tower

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