Deep Learning Training in Busan

This instructor-led live training, offered in Busan (online or onsite), targets intermediate developers, data scientists, and AI practitioners eager to harness TensorFlow Lite for Edge AI solutions.

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

• Comprehend the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Busan (online or onsite) is designed for advanced-level professionals seeking to deepen their understanding of computer vision and explore TensorFlow’s capabilities for developing sophisticated vision models using Google Colab.

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

• Build and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led, live training in Busan (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

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

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilize advanced features of TensorFlow for deep learning.

This instructor-led, live training in Busan (online or onsite) is aimed at advanced-level professionals who wish to specialize in cutting-edge deep learning techniques for NLU.

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

• Understand the key differences between NLU and NLP models.
• Apply advanced deep learning techniques to NLU tasks.
• Explore deep architectures such as transformers and attention mechanisms.
• Leverage future trends in NLU for building sophisticated AI systems.

This instructor-led, live training in Busan (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 instructor-led, live training in Busan (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

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

• Grasp advanced deep learning architectures and techniques for text-to-image generation.
• Develop complex models and implement optimizations for high-quality image synthesis.
• Enhance performance and scalability for large datasets and complex models.
• Refine hyperparameters to improve model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools

This instructor-led, live training in Busan (online or onsite) is designed for advanced professionals aiming to use AI techniques to revolutionize drug discovery and development processes.

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

• Understand the role of AI in drug discovery and development.
• Apply machine learning techniques to predict molecular properties and interactions.
• Use deep learning models for virtual screening and lead optimization.
• Integrate AI-driven approaches into the clinical trial process.

This instructor-led, live training in Busan (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

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

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

This instructor-led live training in Busan (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.

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

• Set up a development environment that includes a popular LLM.
• Create a basic LLM and fine-tune it on a custom dataset.
• Use LLMs for different natural language tasks such as text summarization, question answering, text generation, and more.
• Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.

This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

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

• Understand the principles of Stable Diffusion and how it works for image generation.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

In this instructor-led, live training session Busan, participants will explore the most relevant and cutting-edge machine learning techniques in Python by building a series of demonstration applications that utilize image, music, text, and financial data.

Upon completion of this training, participants will be able to:

• Implement machine learning algorithms and techniques to solve complex problems.
• Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
• Maximize the potential of Python algorithms.
• Utilize libraries and packages such as NumPy and Theano.

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 Busan (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 instructor-led, live training (online or onsite) is designed for data scientists who want to use TensorFlow to analyze potential fraud data.

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

• Create a fraud detection model in Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.

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

• Build a deep learning model
• Automate data labeling
• Work with models from Caffe and TensorFlow-Keras
• Train data using multiple GPUs, the cloud, or clusters

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This instructor-led, live training in Busan (online or onsite) is designed for developers and data scientists who want to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and more.

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

• Install and configure TensorFlow 2.x.
• Understand the advantages of TensorFlow 2.x over previous versions.
• Construct deep learning models.
• Implement an advanced image classifier.
• Deploy deep learning models to the cloud, mobile devices, and IoT devices.

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