London Circuit

Tower A, Canberra City West, Canberra, Austl. Cap. Terr., Australia, 2601

7 London Circuit, Canberra is in the heart of Australia's capital city. Located on the fifth floor of a premium office building, the centre is a short distance from Australia's Parliament House and several of Canberra's landmark buildings including the Reserve Bank, Canberra Law Courts and the Rydges Canberra. Canberra's main industries include IT, service industries, government administration and defence. Canberra benefits from having one of the lowest unemployment rates in Australia with the economy continuing to grow. The centre is just a 10-minute drive from Canberra Airport and is well placed for local amenities such as shops, restaurants and hotels. The building is named after the hexagonal-shaped London Circuit road which surrounds the city centre.

Explore Our Courses

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

 21 Hours

This instructor-led, live training in Canberra (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 Canberra (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

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

This instructor-led, live training in Canberra (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.

  • 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

This instructor-led, live training in Canberra (online or onsite) is aimed at developers and data scientists who wish to learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.

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

  • Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning.
  • Apply advanced Reinforcement Learning algorithms to solve real-world problems.
  • Build a Deep Learning Agent.
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Fundamentals of Reinforcement Learning

 21 Hours

This instructor-led, live training in Canberra (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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Last Updated:

Provisional Upcoming Courses (Require 5+ participants)

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

2025-08-06 09:30
21 hours
London Circuit
10500 AUD (Online)
20790 AUD (Classroom)

Reinforcement Learning with Google Colab

2025-08-25 09:30
28 hours
London Circuit
10000 AUD (Online)
23720 AUD (Classroom)

Fundamentals of Reinforcement Learning

2025-09-03 09:30
21 hours
London Circuit
10500 AUD (Online)
20790 AUD (Classroom)

Reinforcement Learning with Google Colab

2025-09-22 09:30
28 hours
London Circuit
10000 AUD (Online)
23720 AUD (Classroom)

Fundamentals of Reinforcement Learning

2025-10-01 09:30
21 hours
London Circuit
10500 AUD (Online)
20790 AUD (Classroom)

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