DC, Washington - Street Northwest

20 F Street Northwest, Washington, United States, 20001
The Center is located on Capitol Hill, one block from Union Station Metro and 15 minutes from Washington Reagan National Airp...

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Large Language Models (LLMs) and Reinforcement Learning (RL)

 21 Hours
This instructor-led, live training in Washington D.C. (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.
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Deep Reinforcement Learning with Python

 21 Hours
This instructor-led, live training in Washington D.C. (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.
  • Build a Deep Learning Agent.
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Reinforcement Learning with Java

 21 Hours
This instructor-led, live training in Washington D.C. (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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Fundamentals of Reinforcement Learning

 21 Hours
This instructor-led, live training in Washington D.C. (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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