Online or onsite, instructor-led live Reinforcement Learning training courses demonstrate through interactive hands-on practice how to create and deploy a Reinforcement Learning system.
Reinforcement Learning training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Cardiff onsite live Reinforcement Learning trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Cardiff
Radisson Blu Hotel, Meridian Gate - Bute Terrace, Cardiff, united kingdom, CF10 2FL
The Radisson Blu Hotel in Cardiff city centre is the perfect hub for your Welsh adventure
Close to several public transportation options, our hotel in Cardiff puts the city centre at your fingertips. Catch a train or a bus at one of the nearby stations, or take the M4 motorway and drive wherever you want to go in Wales and beyond. For those flying into the city, the Cardiff International Airport is just a 30-minute drive from the hotel. You’ll find parking around the hotel at the John Lewis, St David’s II and NCP Pellet Street car parks, plus some parking at the hotel. Enjoy shopping and dining within walking distance of the hotel, and explore the colourful history of this thriving capital city.
The hotel is located on Bute Terrace providing easy access to the M4 at junction 32 only 6 km away.
The central train and bus station is located within a five-minute walk from the hotel.
Cardiff International Airport is located 24 km from the hotel and can be reached by bus, train or taxi.
This instructor-led, live training in Cardiff (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 Cardiff (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.
This instructor-led, live training in Cardiff (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.
Deep Reinforcement Learning (DRL) combines reinforcement learning principles with deep learning architectures to enable agents to make decisions through interaction with their environments. It underpins many modern AI advancements such as 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 using reward-based learning.
This instructor-led, live training (online or onsite) is aimed at intermediate-level developers and data scientists who wish to learn and apply Deep Reinforcement Learning techniques to build intelligent agents capable of autonomous decision-making in complex environments.
By the end of this training, participants will be able to:
Understand the theoretical foundations and mathematical principles of Reinforcement Learning.
Implement key RL algorithms including Q-Learning, Policy Gradients, and Actor-Critic methods.
Build and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
Apply DRL to real-world applications such as games, robotics, and decision optimization.
Troubleshoot, visualize, and optimize training performance using modern tools.
Format of the Course
Interactive lecture and guided discussion.
Hands-on exercises and practical implementations.
Live coding demonstrations and project-based applications.
Course Customization Options
To request a customized version of this course (e.g., using PyTorch instead of TensorFlow), please contact us to arrange.
This instructor-led, live training in Cardiff (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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