Deep Learning Training in Hsinchu

This instructor-led live training in Hsinchu (online or onsite) is designed for intermediate-level developers, data scientists, and AI practitioners eager to utilize TensorFlow Lite for Edge AI applications.

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

• Understand 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 Hsinchu (online or onsite) is aimed at advanced-level professionals who wish 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 Hsinchu (online or onsite) is designed for intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

Upon completing this training, participants will be able to:

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

Designed for advanced professionals aiming to specialize in cutting-edge deep learning techniques for NLU, this instructor-led live training (online or onsite) offers targeted instruction.

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 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 instructor-led, live training in Hsinchu (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:

• Understand advanced deep learning architectures and techniques for text-to-image generation.
• Implement complex models and optimizations for high-quality image synthesis.
• Optimize performance and scalability for large datasets and complex models.
• Tune hyperparameters for better model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools

This instructor-led, live training in Hsinchu (online or onsite) is aimed at advanced professionals who wish to leverage 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 Hsinchu (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 session in Hsinchu (online or onsite) is designed for beginner to intermediate-level developers who wish to utilize Large Language Models for various natural language tasks.

Upon completing this training, participants will be able to:

• Establish a development environment integrated with a popular LLM.
• Construct a basic LLM and fine-tune it using custom datasets.
• Apply LLMs to diverse natural language tasks such as text summarization, question answering, text generation, and more.
• Debug and evaluate LLMs utilizing tools like 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 in Hsinchu, participants will master the most current and advanced machine learning techniques in Python by developing a series of demonstration applications that process image, music, text, and financial data.

Upon completing this training, participants will be capable of:

• Implementing machine learning algorithms and techniques to address complex problems.
• Applying deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
• Maximizing the potential of Python algorithms.
• Utilizing libraries and packages such as NumPy and Theano.

Applied AI from Scratch in Python provides programmers and data analysts with the essential techniques required to construct machine learning solutions from the ground up using Python. The course covers the fundamental principles of supervised learning, including classification and regression, as well as unsupervised learning methods such as clustering and anomaly detection, alongside advanced neural network architectures. It explores proven strategies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. This helps professionals implement functional ML models, assess algorithmic constraints, and complete applied projects designed to solve real-world problems.

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.

Delving into the fundamentals of artificial intelligence reveals how intelligent technologies are transforming digital strategy, automation, and decision-making across enterprise operations. This course explores core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside areas such as communication, perception, and autonomous action. It equips executives and architects with the insights needed to evaluate AI-driven transformation opportunities, assess emerging technology trends, and integrate practical intelligent solutions to accelerate business agility.

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.

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 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.

This instructor-led, live training in Hsinchu (online or onsite) is designed for data scientists who wish to utilize TensorFlow for analyzing potential fraud data.

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

• Create a fraud detection model using 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 utilize Matlab to design, construct, and visualize a convolutional neural network for image recognition.

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

• Construct a deep learning model
• Automate the data labeling process
• 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

• A combination of lectures, discussions, exercises, and extensive hands-on practice

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

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

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

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