Whether delivered online or onsite, our instructor-led live Explainable AI (XAI) training programs utilize interactive, hands-on practice to demonstrate how to leverage explainable AI techniques for interpreting, understanding, and communicating the decision-making processes of AI models. This approach significantly enhances transparency and builds trust in AI systems.
Explainable Artificial Intelligence (XAI) training is offered as either "online live training" or "onsite live training". Online live training, also referred to as "remote live training", is facilitated through an interactive, remote desktop. Onsite live training can be conducted either locally at the customer's premises in Bangkok or within NobleProg's corporate training centers in Bangkok.
NobleProg -- Your Local Training Provider
Empire Tower
1, South Sathorn Road,, Bangkok, thailand, 10120
Ways to get there
BTS Skytrain: Take the Silom Line and get off at BTS Chong Nonsi. The Empire is within walking distance; the building has access from the Sathorn/Naradhiwas area.
BTS Saint Louis:Saint Louis station is also nearby, around a 7-minute walk according to current location information.
BRT: Get off at BRT Sathorn. It's approximately a 2–4 minute walk to the building.
MRT:MRT Silom is another option, followed by a walk toward Sathorn.
Taxi/Grab: Give the driver: “The Empire, 1 South Sathorn Road, Sathon, Bangkok 10120.” The official building site confirms this address.
By car: The building can be accessed from Sathorn Road or Naradhiwas Rajanagarinda Road.
This live, instructor-guided training Bangkok (available online or on-site) is specifically designed for mid-level professionals aiming to leverage Explainable AI techniques to secure fairness, transparency, and ethical integrity in AI systems.
By the conclusion of this program, participants will be empowered to:
Comprehend the significance of XAI in shaping ethical AI systems.
Utilize XAI strategies to spot and alleviate bias within AI models.
Maintain transparency throughout AI model decision-making procedures.
Ensure AI development complies with ethical norms and regulatory requirements.
This live, instructor-led training in Bangkok (online or onsite) is tailored for advanced professionals aiming to explore state-of-the-art XAI techniques for deep learning, with a specific focus on creating interpretable AI systems.
By the end of the program, participants will have the ability to:
Grasp the difficulties associated with explainability in deep learning.
Deploy advanced XAI techniques for neural networks.
Decipher the decisions made by deep learning models.
Analyze the trade-offs between high performance and transparency.
This instructor-led, live training session in Bangkok (delivered online or onsite) is curated for advanced-level professionals aiming to refine their expertise in XAI techniques for complex AI models.
By the conclusion of this training, participants will have the capability to:
Integrate state-of-the-art XAI techniques into AI models.
Interpret the logic and decisions of deep learning models.
Apply sophisticated explainability methods, whether model-agnostic or model-specific.
Resolve issues concerning AI transparency in complex systems.
This instructor-led live session in Bangkok (online or onsite) is tailored for intermediate to advanced professionals aiming to integrate transparent AI systems into real-world applications.
By the end of the program, participants will be equipped to:
Understand the significance of transparency in AI models.
Deploy advanced XAI methods to interpret intricate models.
Boost model clarity using SHAP, LIME, and related tools.
Manage ethical issues and promote fairness in AI systems.
This live, instructor-led training in Bangkok (delivered online or onsite) is tailored for beginner-level professionals seeking to master the basics of Explainable AI and its contribution to building responsible AI systems.
Upon completion of this training, participants will be able to:
Comprehend the core principles of Explainable AI.
Recognize the critical importance of transparency and interpretability in AI models.
Acquire basic techniques for enhancing AI model explainability.
Apply XAI methods to straightforward machine learning models.
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