Delivered as instructor-led live sessions, either online or onsite, our Reinforcement Learning training provides an interactive, hands-on experience to guide you in building and deploying effective Reinforcement Learning systems.
You can choose between "online live training" and "onsite live training." Online live training (also known as "remote live training") utilizes an interactive remote desktop environment. For onsite live training, sessions can be hosted locally at your premises in Bhutan or at NobleProg corporate training centers in Bhutan.
NobleProg -- Your Local Training Provider
Bhutan, Thimphu - Classroom
near Le Méridien , Chorten Lam, Thimphu, Bhutan, 11001
Set in Thimphu, this classroom is well located in Chorten Lam with all amenities and WiFi.
For Sales Enquires and Meetings
All our centres have batches running on weekdays and weekends hence, please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
Bhutan, Paro - Classroom
near Le Méridien Riverfront, thimphu hwy, Shaba, Paro, Bhutan, 12001
Set in Paro, this classroom is well located near Paro-Thimphu Highway around 4 km from the airport, and 7 km from Rinpung Dzong, and possess all amenities and WiFi.
For Sales Enquires and Meetings
All our centres have batches running on weekdays and weekends hence, please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
This instructor-led, live training in Bhutan (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 Bhutan (online or onsite) is targeted at advanced 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.
This instructor-led, live training in Bhutan (online or onsite) targets advanced professionals seeking to deepen their understanding of reinforcement learning and its practical application in AI development using Google Colab.
By the conclusion of this training, participants will be able to:
Comprehend the core concepts of reinforcement learning algorithms.
Implement reinforcement learning models using TensorFlow and OpenAI Gym.
Develop intelligent agents that learn via trial and error.
Optimize agent 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.
Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make decisions via interaction with their surroundings. It serves as the foundation for numerous contemporary AI innovations, including self-driving cars, robotics control, algorithmic trading, and adaptive recommendation systems. DRL enables artificial agents to learn strategies, optimize policies, and make autonomous decisions through trial and error using reward-based learning.
This instructor-led live training (available online or onsite) targets intermediate-level developers and data scientists keen on learning and applying Deep Reinforcement Learning techniques to construct intelligent agents capable of autonomous decision-making within complex environments.
Upon completion of this training, participants will be able to:
Comprehend the theoretical foundations and mathematical principles of Reinforcement Learning.
Implement core RL algorithms, including Q-Learning, Policy Gradients, and Actor-Critic methods.
Construct and train Deep Reinforcement Learning agents utilizing TensorFlow or PyTorch.
Apply DRL to real-world scenarios such as gaming, robotics, and decision optimization.
Troubleshoot, visualize, and optimize training performance using modern tools.
Format of the Course
Interactive lectures coupled with guided discussions.
Hands-on exercises and practical implementations.
Live coding demonstrations and project-based applications.
Course Customization Options
To request a customized version of this course (e.g., substituting PyTorch for TensorFlow), please contact us to arrange.
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