Neural Networks Training in Seoul

This instructor-led, live training in Seoul (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems.

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

• Understand the challenges of explainability in deep learning.
• Implement advanced XAI techniques for neural networks.
• Interpret decisions made by deep learning models.
• Evaluate the trade-offs between performance and transparency.

This course provides programmers and data analysts with the foundational techniques required to build machine learning solutions from the ground up using Python. It covers the core principles of supervised learning (classification and regression), unsupervised learning (clustering and anomaly detection), and advanced neural network architectures. Participants will examine proven methods for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The program empowers professionals to implement practical ML models, assess algorithm limitations, and complete applied projects aimed at solving real-world problems.

Deep Reinforcement Learning (DRL) integrates the principles of reinforcement learning with deep learning structures, empowering agents to make decisions through environmental interaction. This technology drives numerous modern AI innovations, including autonomous vehicles, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to refine policies, learn strategies, and execute autonomous decisions by leveraging trial-and-error mechanisms and reward-based feedback.

This instructor-led training session, available both online and onsite, is designed for intermediate developers and data scientists looking to master and apply Deep Reinforcement Learning techniques. The goal is to equip participants with the skills necessary to develop intelligent agents capable of making autonomous decisions in complex scenarios.

Upon completing this course, participants will be able to:

• Grasp the theoretical foundations and mathematical core of Reinforcement Learning.
• Code essential RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents utilizing TensorFlow or PyTorch.
• Deploy DRL solutions for practical use cases, including gaming, robotics, and optimization tasks.
• Utilize contemporary tools to visualize, troubleshoot, and optimize training performance.

Course Format

• Engaging lectures paired with guided discussions.
• Practical exercises and real-world implementation scenarios.
• Live coding sessions and project-based applications.

Customization Options

• For customized course versions (e.g., switching from TensorFlow to PyTorch), please reach out to us to make arrangements.

An examination of the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategies, automation, and decision-making processes throughout enterprise operations. This course covers core concepts including the history of AI, frameworks for problem-solving, methods for representing knowledge, reasoning under uncertainty, and various machine learning paradigms, as well as aspects of communication, perception, and autonomous action. It is designed to help executives and architects evaluate opportunities for AI-driven transformation, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This course explores the application of AI—specifically Machine Learning and Deep Learning—within the automotive industry. It aims to identify technologies suitable for various in-car scenarios, ranging from basic automation and image recognition to autonomous decision-making.

An Artificial Neural Network is a computational data model utilized in the development of Artificial Intelligence (AI) systems designed to perform 'intelligent' tasks. Neural Networks are widely employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning serves as a specialized subset of Machine Learning.

This instructor-led, live training in Seoul (online or onsite) offers a foundational introduction to the fields of pattern recognition and machine learning. It explores practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

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

• Apply fundamental statistical methods to pattern recognition.
• Leverage key models such as neural networks and kernel methods for effective data analysis.
• Deploy advanced techniques to solve complex problems.
• Enhance prediction accuracy by integrating various models.

This course provides foundational knowledge of neural networks and their broader role within machine learning and deep learning algorithms and applications.

The first segment (40%) of the training emphasizes core fundamentals, equipping you to select appropriate technologies such as TensorFlow, Caffe, Theano, DeepDrive, and Keras.

The second segment (20%) introduces Theano, a Python library designed to simplify the development of deep learning models.

The third segment (40%) focuses extensively on TensorFlow, the open-source software library for deep learning developed by Google. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers aiming to utilize TensorFlow for their deep learning projects.

Upon completion, participants will:

• gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend the structure and deployment mechanisms of TensorFlow
• possess the skills to manage installation, production environment setup, architecture tasks, and configuration
• be capable of assessing code quality, performing debugging, and monitoring systems
• be able to implement advanced production-level tasks, including model training, graph construction, and logging