Online Reinforcement Learning Training in US

This instructor-led, live training (online or onsite) is designed for intermediate-level data scientists who aim to achieve a thorough 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: - Comprehend the components and functionality of transformer models. - Optimize and fine-tune LLMs for specific tasks and applications. - Grasp the core principles and methodologies of reinforcement learning. - Understand how reinforcement learning techniques can improve the performance of LLMs for government use.

This instructor-led, live training (online or onsite) is aimed at advanced-level machine learning engineers and AI researchers who wish to apply Reinforcement Learning from Human Feedback (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 its importance 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 to support production-grade AI systems for government.

This instructor-led, live training in US (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 for government 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 agent 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, enhancing decision-making processes for government.

Deep Reinforcement Learning (DRL) integrates the principles of reinforcement learning with deep learning architectures, enabling agents to make decisions through interaction with their environments. This technology is pivotal in driving many modern artificial intelligence advancements, including self-driving vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL allows an artificial agent to learn strategies, optimize policies, and make autonomous decisions based on trial-and-error learning using a reward system.

This instructor-led, live training (online or onsite) is designed for intermediate-level developers and data scientists who wish to gain proficiency in Deep Reinforcement Learning techniques. The goal is to build intelligent agents capable of autonomous decision-making in complex environments, which can be particularly useful for government applications.

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

• Understand the theoretical foundations and mathematical principles underlying Reinforcement Learning.
• Implement key RL algorithms such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Develop and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to real-world scenarios, including games, robotics, and decision optimization for government use cases.
• Troubleshoot, visualize, and optimize the training performance of DRL models using modern tools.

Format of the Course

• Interactive lectures and guided discussions.
• Hands-on exercises and practical implementations.
• Live coding demonstrations and project-based applications.

Course Customization Options

• To request a customized version of this course, such as using PyTorch instead of TensorFlow, please contact us to arrange.

This instructor-led, live training in US (online or onsite) is designed for data scientists who wish to extend their expertise beyond traditional machine learning methods to develop computer programs capable of problem-solving without the use of labeled data and large datasets. By the end of this training, participants will be able to: - Install and utilize the necessary libraries and programming languages required for implementing Reinforcement Learning. - Develop a software agent that can learn through feedback mechanisms rather than supervised learning techniques. - Program an agent to address problems involving sequential and finite decision-making processes. - Apply this knowledge to create software solutions that can learn in a manner akin to human learning, enhancing capabilities for government applications.