Nagoya - Meieki Minami

5F & 6F Links Meieki Building, 5-31-10 Meieki, Nakamura-ku, Aichi-ken, Nagoya, japan, 450-0002

Nagoya is the third largest city in Japan. This open office is located in the district surrounding Nagoya station. Since 2015, landmark buildings kept inaugurating around the station and by 2017, it is predictable that many corporations will choose to be in this Meieki area. Furthermore, with the magnetic-levitated linear shinkansen to start service in 2027, Nagoya has strategic advantage. Nagoya and Aichi area is known for its auto industry like Toyota. Besides automobiles, there are many head offices of Japanese manufacturers. With their affiliated companies and partners located in the same area, the city itself is a business complex. In recent years, forward-thinking IT companies are also trying to get a foothold in Nagoya. Besides all the attention-gathering news on the redevelopment, Meieki area has good access to Tokyo, Osaka and other major cities. It is also convenient for sales activity and commute. Therefore, it is a popular yet practical business district.

Explore Our Courses

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

 21 Hours
This instructor-led, live training in Nagoya (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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Reinforcement Learning with Google Colab

 28 Hours
This instructor-led, live training in Nagoya (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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Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF)

 14 Hours
This instructor-led, live training in Nagoya (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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Deep Reinforcement Learning with Python

 21 Hours
This instructor-led, live training in Nagoya (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 Nagoya (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

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

2025-06-09 10:00
21 hours
Nagoya - Meieki Minami
¥ 683316 ~ (Online)
¥ 1366632 ~ (Classroom)

Fundamentals of Reinforcement Learning

2025-06-23 10:00
21 hours
Nagoya - Meieki Minami
¥ 683316 ~ (Online)
¥ 1366632 ~ (Classroom)

Reinforcement Learning with Google Colab

2025-07-07 10:00
28 hours
Nagoya - Meieki Minami
¥ 911088 ~ (Online)
¥ 1822176 ~ (Classroom)

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

2025-07-21 10:00
21 hours
Nagoya - Meieki Minami
¥ 683316 ~ (Online)
¥ 1366632 ~ (Classroom)

Deep Reinforcement Learning with Python

2025-08-04 10:00
21 hours
Nagoya - Meieki Minami
¥ 683316 ~ (Online)
¥ 1366632 ~ (Classroom)

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

2025-08-14 10:00
14 hours
Nagoya - Meieki Minami
¥ 455544 ~ (Online)
¥ 911088 ~ (Classroom)

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