Neural Networks Training in Auckland

This instructor-led, live training in Auckland (online or on-site) is designed for advanced-level professionals seeking to explore state-of-the-art XAI techniques for deep learning models, with a particular 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 is a four-day course introducing AI and its application using the Python programming language. There is an option to undertake an additional day of an AI project upon completion of this course.

Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make decisions through interaction with their environments. It underpins many modern AI advancements, including self-driving vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL enables an artificial agent to learn strategies, optimise policies, and make autonomous decisions based on trial and error using reward-based learning.

This instructor-led, live training (available online or on-site) is designed for intermediate-level developers and data scientists who wish to learn and apply Deep Reinforcement Learning techniques to build intelligent agents capable of autonomous decision-making in complex environments.

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

• Understand the theoretical foundations and mathematical principles of Reinforcement Learning.
• Implement key 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 optimisation.
• Troubleshoot, visualise, and optimise training performance using modern tools.

Format of the Course

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

Course Customisation Options

• To request a customised version of this course (for example, using PyTorch instead of TensorFlow), please contact us to arrange.

This course has been developed for managers, solutions architects, innovation officers, CTOs, software architects, and anyone seeking an overview of applied artificial intelligence and the near-term outlook for its evolution.

This course covers AI (emphasising Machine Learning and Deep Learning) in the automotive industry. It helps determine which technologies can potentially be used in a variety of automotive scenarios—from simple automation and image recognition to autonomous decision-making.

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

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

• Grasp fundamental 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 to tackle problem-solving tasks.

An Artificial Neural Network is a computational data model employed in the development of Artificial Intelligence (AI) systems capable of performing “intelligent” tasks. Neural Networks are widely used in Machine Learning (ML) applications, which represent one implementation of AI. Deep Learning is a specialised subset of ML.

This is a four-day course introducing AI and its applications. There is an option to undertake an additional day to complete an AI project upon finishing this course.

This instructor-led, live training in Auckland (online or on-site) is designed for intermediate-level data scientists and statisticians who wish to prepare data, build models, and effectively apply machine learning techniques within 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 visualise results effectively.
• Apply machine learning techniques to real-world, sector-specific scenarios.

An Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which themselves represent one implementation of AI. Deep Learning is a subset of ML.

This instructor-led, live training in Auckland (online or on-site) is tailored for researchers and developers who wish to use Chainer to build and train neural networks in Python while ensuring the code remains easy to debug.

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

• Set up the necessary development environment to begin building neural network models.
• Define and implement neural network models using clear and comprehensible source code.
• Execute examples and modify existing algorithms to optimise deep learning training models while leveraging GPUs for high-performance computing.

This instructor-led, live training in Auckland (online or onsite) introduces participants to the field of pattern recognition and machine learning. It explores practical applications across 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 such as neural networks and kernel methods for data analysis.
• Implement advanced techniques to tackle complex problem-solving challenges.
• Improve prediction accuracy by combining different models.

In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualise 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 labelling
• 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 learn the fundamentals of neural networks and their practical applications.

This classroom-based training session will include presentations, computer-based examples, and case study exercises using relevant neural and deep learning libraries.

This course provides an introduction to applying neural networks to real-world problems using R software.

This course begins by providing you with conceptual knowledge of neural networks, machine learning algorithms, and deep learning (including algorithms and applications).

Part 1 (40%) of this training focuses on fundamentals while helping you select the right technology, such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

Part 2 (20%) introduces Theano—a Python library that simplifies writing deep learning models.

Part 3 (40%) is extensively based on TensorFlow, the API for Google's open-source software library for Deep Learning. All examples and hands-on activities will be conducted using TensorFlow.

Audience

This course is designed for engineers seeking to use TensorFlow for their Deep Learning projects.

Upon completing this course, delegates will:

• have a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• understand TensorFlow's structure and deployment mechanisms
• be able to carry out installation, production environment, and architecture tasks and configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production tasks such as training models, building graphs, and logging