Deep Learning Training in Auckland

This instructor-led, live training in Auckland (online or on-site) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

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

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimise AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Use tools and techniques for model conversion and optimisation.
• Implement practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Auckland (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.
• Visualise and interpret the results of image classification models.

This instructor-led, live training in Auckland (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.
• Utilise advanced features of TensorFlow for deep learning.

This instructor-led, live training in Auckland (online or on-site) is aimed at advanced-level professionals who wish to specialise 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 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 instructor-led, live training in Auckland (delivered online or on-site) is designed for intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to deepen their expertise 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 optimisation strategies for high-quality image synthesis.
• Optimise performance and scalability for large datasets and complex models.
• Tune hyperparameters to improve model performance and generalisation.
• Integrate Stable Diffusion with other deep learning frameworks and tools.

This instructor-led, live training in Auckland (online or on-site) is designed for advanced-level professionals seeking to harness AI techniques to revolutionise 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 optimisation.
• Integrate AI-driven approaches into the clinical trial process.

This instructor-led, live training in Auckland (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 Auckland (available online or on-site) is designed for developers at the beginner to intermediate level who wish to leverage 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.
• Apply LLMs to different natural language tasks such as text summarisation, 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 (online or on-site) 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.
• Optimise the performance and stability of Stable Diffusion models.

In this instructor-led, live training in Auckland, participants will explore the most relevant and cutting-edge machine learning techniques in Python by building a series of demo applications that incorporate image, music, text, and financial data.

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

• Implement machine learning algorithms and techniques to tackle complex problems.
• Apply deep learning and semi-supervised learning approaches to applications involving image, music, text, and financial data.
• Maximise the potential of Python algorithms.
• Utilise libraries and packages such as NumPy and Theano.

Applied AI from Scratch in Python equips programmers and data analysts with foundational techniques for building machine learning solutions from the ground up using Python. Covers core principles of supervised learning classification and regression, unsupervised learning clustering and anomaly detection, and advanced neural network architectures. Examines proven methods for working with scikit-learn, Apache Spark MLlib, and Jupyter notebooks for hands-on AI development. Helps professionals implement practical ML models, evaluate algorithm limitations, and complete applied projects for real-world problem solving.

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.

A deep dive into the fundamentals of artificial intelligence illustrates how intelligent technology is transforming digital strategy, automation, and decision-making processes within enterprise operations. This course examines 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 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.

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 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.

This instructor-led, live training in Auckland (available online or on-site) is designed for data scientists who wish to leverage TensorFlow to analyse potential fraud data.

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

• Develop a fraud detection model using Python and TensorFlow.
• Construct linear regressions and linear regression models to predict fraudulent activity.
• Build an end-to-end AI application for analysing fraud data.

During this instructor-led, live training session, participants will learn how to utilise Matlab to design, construct, and visualise 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 labelling process
• Utilise 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 Auckland (online or on-site) is designed for developers and data scientists who wish to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and more.

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

• Install and configure TensorFlow 2.x.
• Understand the advantages of TensorFlow 2.x over earlier versions.
• Build deep learning models.
• Implement advanced image classifiers.
• Deploy deep learning models to the cloud, mobile devices, and IoT platforms.

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