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. Onsite live Reinforcement Learning trainings in Southampton can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Southampton
International House, Southampton International Business Park, Southampton, united kingdom, SO18 2RZ
This centre occupies a great position - highly visible from the M27 motorway. The building's focal point is a beautiful at...
This centre occupies a great position - highly visible from the M27 motorway. The building's focal point is a beautiful atrium flooding the reception area with natural light. A comfortable, professional business lounge and a large breakout area where you can relax or network add to the welcoming atmosphere. This is an outstanding south coast location next to Southampton airport with air, road and rail transport links on the doorstep. Regular launches and exhibitions held at the centre have contributed to its reputation as a hub of business and cultural activity. Southampton is an innovative and enterprising city with thousands of thriving organisations and tens of thousands of productive people. The most significant sectors in terms of businesses and employee numbers are distribution, tourism, banking and finance-related activities. It also has one of the top 10 research-led universities in the UK, which provides a wide range of services to the business community.
This instructor-led, live training in Southampton (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 Southampton (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.
This instructor-led, live training in Southampton (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.
This instructor-led, live training in Southampton (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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