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. Penang Island onsite live Reinforcement Learning trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Bayan Lepas, iDEAL
73-3-1, Ideal@The One, Jalan , Kuala Lumpur, malaysia, 11950
The iDEAL Bayan Baru is located in the township near Bayan Lepas, Penang International Airport and also the second Penang Bri...
The iDEAL Bayan Baru is located in the township near Bayan Lepas, Penang International Airport and also the second Penang Bridge. The Building is located in Bayan Baru CBD, and within Bayan Lepas Free Trade Zone. Due to its proximity to the free trade zone that is home to many MNCs, this South-West District is one of the preferred retail/shopping destination s for working professionals, families, visitors and MICE (meeting, incentive, convention, exhibition).
Bayan Baru as part of the Penang Cybercity phase 1, has been identified as a key focus market in Malaysia’s development plan for 2015. It has easy access to corporate offices, restaurants, banks and many more amenities. Our center provide flexible, fully-equipped office workspaces for businesses large and small, branch or sales offices, agencies and consultancies, with comprehensive services at a reasonable price.
This instructor-led, live training in Penang Island (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 Penang Island (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 Penang Island (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 Penang Island (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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