日本の強化学習トレーニング

This instructor-led, live training in 日本 (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.

This instructor-led, live training in 日本 (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.

This instructor-led, live training in 日本 (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.

This instructor-led, live training in 日本 (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.