Neural Networks Training in Hsinchu

This instructor-led, live training in Hsinchu (online or onsite) targets 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 four-day course introduces the fundamentals of AI and its practical applications using Python. Upon completion, participants may choose to extend their learning by adding an extra day dedicated to working on an AI project.

Deep Reinforcement Learning (DRL) merges reinforcement learning principles with deep learning architectures, empowering agents to make decisions through interaction with their environments. This technology drives many modern AI advancements, including self-driving vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL enables artificial agents to learn strategies, optimize policies, and make autonomous decisions via trial-and-error reward-based learning.

This instructor-led live training (available online or onsite) targets intermediate-level developers and data scientists eager to learn and apply Deep Reinforcement Learning techniques to construct intelligent agents capable of autonomous decision-making in complex environments.

Upon completing this training, participants will be able to:

• Grasp the theoretical foundations and mathematical principles of Reinforcement Learning.
• Implement core RL algorithms, including Q-Learning, Policy Gradients, and Actor-Critic methods.
• Build and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to real-world applications such as games, robotics, and decision optimization.
• Troubleshoot, visualize, and optimize training performance using contemporary tools.

Course Format

• Interactive lectures and guided discussions.
• Hands-on exercises and practical implementations.
• Live coding demonstrations and project-based applications.

Customization Options

• To request a customized version of this course (e.g., utilizing PyTorch instead of TensorFlow), please contact us to arrange.

This course is designed for managers, solutions architects, innovation officers, CTOs, software architects, and anyone seeking a comprehensive overview of applied artificial intelligence and its near-term developmental trajectory.

This course explores the application of AI—focusing on Machine Learning and Deep Learning—within the automotive industry. It aids in identifying technologies that can be applied across various in-car scenarios, ranging from basic automation and image recognition to autonomous decision-making.

This instructor-led live training in Hsinchu (online or onsite) is aimed at beginner-level participants who wish to learn essential concepts in probability, statistics, programming, and machine learning, and apply these to AI development.

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

• Understand basic concepts in probability and statistics, and apply them to real-world scenarios.
• Write and understand procedural, functional, and object-oriented programming code.
• Implement machine learning techniques such as classification, clustering, and neural networks.
• Develop AI solutions using rules engines and expert systems for problem-solving.

An Artificial Neural Network is a computational data model utilized in the development of Artificial Intelligence (AI) systems, enabling them to perform "intelligent" tasks. These networks are frequently employed in Machine Learning (ML) applications, which constitute one form of AI implementation. Deep Learning represents a specialized subset of Machine Learning.

This four-day course provides an introduction to artificial intelligence and its practical applications. Upon completing the course, participants have the option to dedicate an additional day to working on an AI project.

This instructor-led, live training in Hsinchu (online or onsite) is aimed at intermediate-level data scientists and statisticians who wish to prepare data, build models, and apply machine learning techniques effectively in their professional domains.

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

• Understand and implement various Machine Learning algorithms.
• Prepare data and models for machine learning applications.
• Conduct post-hoc analyses and visualize results effectively.
• Apply machine learning techniques to real-world, sector-specific scenarios.

An Artificial Neural Network is a computational data model utilized in developing Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are frequently employed in Machine Learning (ML) applications, which serve as one implementation of AI. Deep Learning constitutes a subset of Machine Learning.

This instructor-led, live training in Hsinchu (online or onsite) is intended for researchers and developers who want to use Chainer to build and train neural networks in Python while ensuring the code is easy to debug.

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

• Set up the necessary development environment to begin creating neural network models.
• Define and implement neural network models using clear and understandable source code.
• Run examples and modify existing algorithms to optimize deep learning training models, leveraging GPUs for high performance.

This instructor-led, live training in Hsinchu (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on 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 core statistical methods to pattern recognition.
• Use key models like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

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 training is designed for individuals seeking to grasp the fundamentals of neural networks and their practical applications.

This classroom-based training program features presentations, hands-on computer-based examples, and case study exercises utilizing relevant neural and deep network libraries.

This course provides an introduction to implementing neural networks for real-world problems using the R programming language.

This course provides foundational knowledge of neural networks and broader machine learning algorithms, with a specific focus on deep learning techniques and their practical applications.

The first section (40% of the curriculum) emphasizes core fundamentals, equipping you with the insights needed to select the most appropriate technology for your needs, such as TensorFlow, Caffe, Theano, DeepDrive, or Keras.

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

The final section (40% of the curriculum) focuses extensively on TensorFlow, Google’s open-source software library for deep learning. All examples and hands-on exercises will be conducted within the TensorFlow framework.

Audience

This course is designed for engineers looking to leverage TensorFlow for their deep learning projects.

Upon completion, participants will:

• Possess a solid understanding of Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs)
• Understand the architecture and deployment mechanisms of TensorFlow
• Be capable of handling installation, production environment setup, and architectural configuration tasks
• Assess code quality, perform debugging, and implement monitoring systems
• Implement advanced production-level tasks such as training models, constructing computational graphs, and setting up logging