日本の強化学習トレーニング

Large Language Models (LLMs) are advanced types of neural networks designed to understand and generate human-like text based on the input they receive. Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative rewards.This instructor-led, live training (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.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Deep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches. This instructor-led, live training (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.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Reinforcement Learning (RL) is a machine learning technique in which a computer program (agent) learns to behave in an environment by performing the actions and receiving feedback on the results of the actions. For each good action, the agent receives positive feedback, and for each bad action, the agent receives negative feedback (penalty). This instructor-led, live training (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.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Reinforcement Learning (RL) is an area of AI (Artificial Intelligence) used to build autonomous systems (e.e., an "agent") that learn by interacting with their environment in order to solve a problem. RL has applications in areas such as robotics, gaming, consumer modeling, healthcare, supply chain management, and more. This instructor-led, live training (online or onsite) is aimed at data scientists who wish to create and deploy a Reinforcement Learning system, capable of making decisions and solving real-world problems within an organization. By the end of this training, participants will be able to:

• Understand the relationships and differences between Reinforcement Learning and machine learning, deep learning, supervised and unsupervised learning.
• Analyze a real-world problem and redefine it as Reinforcement Learning problem.
• Implementing a solution to a real-world problem using Reinforcement Learning.
• Understand the different algorithms available in Reinforcement Learning and select the most suitable one for the problem at hand.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.