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. Kortrijk onsite live Reinforcement Learning trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
You can rent 3 meeting rooms and an indoor terrace in VORK. Coffee, tea and water are always available. Everything is there t...
You can rent 3 meeting rooms and an indoor terrace in VORK. Coffee, tea and water are always available. Everything is there to prepare the meeting rooms yourself.
Would you like a sandwich lunch? Or would you like to combine your meeting with a lunch in the restaurant? It's all possible in VORK!
Everything is available to prepare the meeting rooms yourself. Possibility of lunch (e.g. soup, sandwiches, etc.) or dinner in the restaurant after consultation with the restaurant manager.
Additional facilities: clickshare available, video conference option.
VORK is centrally located in the city and is therefore easily accessible on foot, by bicycle or by public transport. If you come by car, you can park underground or above ground at the following locations:
Underground: Schouwburg parking, Veemarkt parking, K in Kortrijk, Budabrug parking, Houtmarkt parking
Above ground: Station (Conservatoriumplein), Broeltorens (Ijzerkaai), Harbor (Botenkopersstraat), Kortrijk Weide (Nelson Mandelaplein).
The first hour is always free! If you park for longer than one hour, you will have to pay (from €1).
You can also park your car for free at Parking Expo (P+R) and take a free bus from De Lijn to the center. Redeem ticket with the bus driver: free parking + return ticket to the city center.
More information at www.parko.be
This instructor-led, live training in Kortrijk (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 Kortrijk (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 Kortrijk (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 Kortrijk (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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