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 Faro can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Hotel Faro & Beach Club
2, Praça Dom Francisco Gomes 2, 8000-168 Faro, Faro, Portugal, 8000-168 Faro
Situated in the middle of the city center of Faro, Hotel Faro offers stunning views of Ria Formosa and on the historic area, ...
Situated in the middle of the city center of Faro, Hotel Faro offers stunning views of Ria Formosa and on the historic area, being the ideal place for holidays and business. The proximity to Faro International Airport, privileged access to the best beaches in the Algarve and all the services you have in your involvement, make Hotel Faro the perfect choice for your stay. This modern 4-star hotel offers 90 cozy and sophisticated rooms (including 3 suites), a magnificent rooftop with panoramic views of the city where the restaurant are located, the bar and a heated hydrotherapeutic pool. Hotel Faro also offers garage, GYM & Dayspa, 4 multifunctional rooms and a beach club situated on the island of Faro. At the beach season, the offer also includes a daily transfer service to the Beach Club and boating from Ria Formosa, considered one of the 7 wonders of Portugal.
This instructor-led, live training in Faro (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 Faro (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 Faro (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.
This instructor-led, live training in Faro (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.
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