Introduction to the Use of Neural Networks Training Course

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.

This programme is designed for managers, solutions architects, innovation leads, CTOs, software architects, and any professionals seeking a comprehensive understanding of applied artificial intelligence and its projected future developments.

This instructor-led, live training in Malaysia (online or onsite) is designed for 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 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 four-day programme provides an introduction to artificial intelligence and its practical applications. Participants may also opt to extend the course by one day to work on a dedicated AI project upon completion.

This four-day course provides an introduction to Artificial Intelligence (AI) and its practical applications using the Python programming language. Participants may also opt for an extra day to work on a dedicated AI project upon completing the course content.

Delivered by an instructor via live sessions (Malaysia (online or onsite), this programme is designed for intermediate-level data scientists and statisticians who wish to effectively prepare data, construct models, and apply machine learning techniques within their respective professional fields.

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

• Comprehend and implement a variety of machine learning algorithms.
• Prepare data and models for machine learning applications.
• Conduct post-hoc analyses and visualise results effectively.
• Apply machine learning techniques to real-world, sector-specific scenarios.

An Artificial Neural Network is a computational data model utilized in the creation of Artificial Intelligence (AI) systems that can execute "intelligent" tasks. Neural Networks are frequently employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning constitutes a subset of Machine Learning.

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

Upon completion 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.
• Execute examples and modify existing algorithms to optimize deep learning training models, leveraging GPUs for high performance.

This instructor-led, live training in Malaysia (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.

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.

This instructor-led training session includes presentations, computer-based examples, and case study exercises utilizing relevant neural and deep learning libraries.

This course introduces the application of neural networks to real-world problems using R Project software.

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

This instructor-led, live training in Malaysia (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.