Washington, DC - NW Conference Center

20 F St NW, Washington, United States, 20001

The venue is situated in the Capitol Hill area on F Street NW, with easy driving access via Massachusetts Avenue and I-395; paid parking is available on-site and in nearby garages. From Ronald Reagan Washington National Airport (DCA), about 5 miles away, a taxi or rideshare via I-395 North typically takes 15–20 minutes. From Dulles International Airport (IAD), approximately 26 miles away, the trip via the Dulles Access Road and I-66/I-395 takes roughly 35–45 minutes. Public transit is extremely convenient with Union Station Metro just one block away (Red Line) and multiple Metrobus routes along F, H, and G Streets NW.
NobleProg Training Place, City Washington, DC - Regus One Franklin Square

Washington, DC - Regus One Franklin Square

1300 I Street NW, Suite 400E, Washington, United States, 20005

The venue sits prominently at One Franklin Square, in downtown Washington, DC, easily reachable by car via Massachusetts Avenue or I‑395 with paid garage parking available in the Franklin Square complex. From Ronald Reagan Washington National Airport (DCA), around 5 miles away, taxi or rideshare via I‑395 typically takes 15–20 minutes. From Dulles International Airport (IAD), approximately 26 miles away, the journey via the Dulles Access Road and I‑66/I‑395 takes about 35–45 minutes. For public transit, McPherson Square Metro Station (Orange, Silver, and Blue lines) is a short two-block walk, and multiple Metrobus routes run along I Street NW, providing convenient access to the venue.

Explore Our Courses

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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Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF)

 14 Hours

This instructor-led, live training in Washington D.C. (online or onsite) is aimed at advanced-level machine learning engineers and AI researchers who wish to apply RLHF to fine-tune large AI models for superior performance, safety, and alignment.

By the end of this training, participants will be able to:

  • Understand the theoretical foundations of RLHF and why it is essential in modern AI development.
  • Implement reward models based on human feedback to guide reinforcement learning processes.
  • Fine-tune large language models using RLHF techniques to align outputs with human preferences.
  • Apply best practices for scaling RLHF workflows for production-grade AI systems.
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Reinforcement Learning with Google Colab

 28 Hours

This instructor-led, live training in Washington D.C. (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of reinforcement learning and its practical applications in AI development using Google Colab.

By the end of this training, participants will be able to:

  • Understand the core concepts of reinforcement learning algorithms.
  • Implement reinforcement learning models using TensorFlow and OpenAI Gym.
  • Develop intelligent agents that learn through trial and error.
  • Optimize agents' performance using advanced techniques such as Q-learning and deep Q-networks (DQNs).
  • Train agents in simulated environments using OpenAI Gym.
  • Deploy reinforcement learning models for real-world applications.
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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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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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Upcoming Courses

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

2025-09-22 9:30 am
21 hours
Washington, DC - Regus One Franklin Square
$ 4054 (Online)
$ 4054 (Classroom)

Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF)

2025-10-06 9:30 am
14 hours
Washington, DC - NW Conference Center
$ 2703 (Online)
$ 2703 (Classroom)

Fundamentals of Reinforcement Learning

2025-10-20 9:30 am
21 hours
Washington, DC - Regus One Franklin Square
$ 4054 (Online)
$ 4054 (Classroom)

Fundamentals of Reinforcement Learning

2025-11-03 9:30 am
21 hours
Washington, DC - NW Conference Center
$ 4054 (Online)
$ 4054 (Classroom)

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