Deep Learning Training in Kuala Lumpur

This guided, live training in Kuala Lumpur (available online or on-site) is designed for intermediate-level developers, data scientists, and AI practitioners who aim to utilise 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 Kuala Lumpur (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 Kuala Lumpur (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 Kuala Lumpur (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 Kuala Lumpur (online or onsite) is designed for advanced professionals who want to explore cutting-edge XAI techniques for deep learning models, with a strong emphasis 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 Kuala Lumpur (online or onsite) is designed for data scientists, machine learning engineers, deep learning researchers, and computer vision experts at the intermediate to advanced levels who aim to broaden their expertise and proficiency in deep learning for text-to-image generation.

Upon completing this training, participants will be equipped to:

• Grasp advanced deep learning architectures and methodologies for text-to-image generation.
• Deploy complex models and optimisation strategies for high-quality image synthesis.
• Enhance performance and scalability for large-scale datasets and intricate models.
• Calibrate hyperparameters to improve model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools.

This instructor-led live training in Kuala Lumpur (online or onsite) is designed for advanced professionals looking to utilize 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 Kuala Lumpur (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 Kuala Lumpur (online or onsite) is aimed at beginner 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 in Kuala Lumpur, participants will gain mastery over the most pertinent and cutting-edge machine learning techniques in Python. As they construct a series of demonstration applications handling image, music, text, and financial data, they will apply these skills in practice.

Upon completion of 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 fundamental techniques needed to construct machine learning solutions from the ground up using Python. The course covers core principles of supervised learning, including classification and regression, as well as unsupervised learning techniques such as clustering and anomaly detection, alongside advanced neural network architectures. It examines proven methods for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The content enables professionals to implement practical machine learning models, evaluate algorithm limitations, and execute applied projects designed for real-world problem solving.

Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with advanced deep learning architectures, empowering agents to make informed decisions through continuous interaction with their surroundings. This technology drives numerous contemporary AI innovations, including autonomous vehicles, robotics control systems, algorithmic trading platforms, and adaptive recommendation engines. Through reward-based learning, DRL enables artificial agents to refine strategies, optimize policies, and execute autonomous decisions via trial and error.

This live training session, led by an experienced instructor and available online or on-site, is designed for intermediate-level data scientists and developers eager to master and apply Deep Reinforcement Learning techniques. Participants will gain the skills needed to develop intelligent agents capable of autonomous decision-making in complex environments.

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

• Grasp the theoretical foundations and mathematical concepts underlying Reinforcement Learning.
• Deploy core RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL techniques to practical scenarios, including gaming, robotics, and decision optimization.
• Utilize modern tools to troubleshoot, visualize, and optimize training performance.

Course Format

• Engaging lectures combined with guided discussions.
• Practical exercises and hands-on implementation tasks.
• Live coding demonstrations and project-based applications.

Customization Options

• To request a tailored version of this course (for instance, focusing on PyTorch instead of TensorFlow), please reach out to us for arrangement.

A deep dive into the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategies, automation processes, and decision-making capabilities across enterprise operations. The course explores core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside aspects of communication, perception, and autonomous action. It equips executives and architects to assess AI-driven transformation opportunities, evaluate emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This course provides an overview of Artificial Intelligence (with a focus on Machine Learning and Deep Learning) within the automotive industry. It guides participants in identifying technologies that can be applied across various automotive scenarios, ranging from basic automation and image recognition to complex autonomous decision-making.

An Artificial Neural Network serves as a computational data model employed in creating Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. These networks are frequently utilized in Machine Learning (ML) applications, which represent one implementation of AI, while Deep Learning constitutes a subset of ML.

This instructor-led, live training in Kuala Lumpur (online or onsite) offers an introduction to the fields of pattern recognition and machine learning. It covers 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.
• Utilize essential models such as neural networks and kernel methods for data analysis.
• Implement advanced techniques to solve complex problems.
• Enhance prediction accuracy by integrating various models.

This instructor-led live training in Kuala Lumpur (online or on-site) targets software developers, data analysts, and technical professionals who wish to leverage TensorFlow 2.x and Keras to build, train, and deploy deep learning models for computer vision, natural language processing, and multimodal applications.

This instructor-led live training in Kuala Lumpur (online or on-site) is designed for data scientists who wish to utilise TensorFlow to analyse potential fraud data.

By the conclusion 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 analysing 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 Kuala Lumpur (online or onsite) targets developers and data scientists who intend to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and other applications.

By the end 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 on neural networks, machine learning algorithms, and deep learning concepts and applications.

The first part (40%) of this training focuses on fundamentals, helping you select the appropriate technology such as TensorFlow, Caffe, Theano, DeepDrive, and Keras.

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

The third part (40%) is heavily based 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 who wish to utilise TensorFlow for their Deep Learning projects.

Upon completing this course, delegates will be able to:

• gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend TensorFlow’s structure and deployment mechanisms
• perform installation, production environment setup, architecture tasks, and configuration
• assess code quality, debug, and monitor systems
• implement advanced production-level tasks such as training models, constructing graphs, and logging