Beijing Digital Building

2 south street, Beijing, China, 100000

Zhongguancun South Street, Haidian District, Beijing, close to the North Third Ring Road, facing the Friendship Hotel. The location is in the Zhongguancun office building. It is a popular landmark office building with convenient transportation. There are many bus lines in front of the door to the major cities. The building is divided into two bases. Block A is the tallest commercial building in the west of the central axis. It adopts a pillarless large-open design and is suitable for large enterprises. The office space of Block B is flexible and suitable for small and medium-sized office enterprises. There are various restaurants, hotels, shopping malls, supermarkets, entertainment venues and other supporting facilities.

Explore Our Courses

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

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

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

2025-06-23 09:30
14 hours
Beijing Digital Building

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

2025-07-07 09:30
14 hours
Beijing Digital Building

Fundamentals of Reinforcement Learning

2025-07-21 09:30
21 hours
Beijing Digital Building

Fundamentals of Reinforcement Learning

2025-08-04 09:30
21 hours
Beijing Digital Building

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