Deep Learning Training in Adelaide

This instructor-led, live training in Adelaide (available online or onsite) is geared towards 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:

• Comprehend the fundamentals of TensorFlow Lite and its function in Edge AI.
• Construct and enhance AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models across various edge devices.
• Apply tools and techniques for model conversion and optimisation.
• Develop practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Adelaide (online or on-site) is tailored for advanced-level professionals seeking to deepen their grasp of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models via Google Colab.

Upon completion of this training, participants will be capable of:

• Constructing and training convolutional neural networks (CNNs) using TensorFlow.
• Leveraging Google Colab for scalable and efficient cloud-based model development.
• Implementing image preprocessing techniques for computer vision tasks.
• Deploying computer vision models for real-world applications.
• Employing transfer learning to enhance the performance of CNN models.
• Visualising and interpreting the results of image classification models.

This instructor-led, live training in Adelaide (online or onsite) is designed for intermediate-level data scientists and developers who wish to comprehend and apply deep learning techniques within the Google Colab environment.

Upon completion 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 Adelaide (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 Adelaide (online or onsite) is aimed at 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 Adelaide (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.
• Optimise performance and scalability for large datasets and complex models.
• Tune hyperparameters for better model performance and generalisation.
• Integrate Stable Diffusion with other deep learning frameworks and tools

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

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

• Configure a development environment utilising a widely-used LLM.
• Construct a fundamental LLM and fine-tune it on a custom dataset.
• Apply LLMs to diverse natural language tasks such as text summarisation, question answering, and text generation.
• 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 Adelaide, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.

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

• Implement machine learning algorithms and techniques for solving complex problems.
• Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
• Push Python algorithms to their maximum potential.
• Use 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. It 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. The course examines effective strategies for utilising scikit-learn, Apache Spark MLlib, and Jupyter notebooks for practical AI development. It assists professionals in implementing functional ML models, assessing algorithmic limitations, and completing applied projects to solve real-world problems.

Deep Reinforcement Learning (DRL) integrates the principles of reinforcement learning with deep learning architectures, empowering agents to make decisions through direct interaction with their surroundings. This technology drives numerous contemporary AI innovations, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to learn strategies, optimise policies, and make autonomous choices based on trial-and-error mechanisms utilising reward-based learning.

This instructor-led, live training (available online or onsite) is designed for intermediate-level developers and data scientists keen on learning and applying Deep Reinforcement Learning techniques to construct intelligent agents capable of autonomous decision-making within complex environments.

Upon completion of 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.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to practical applications such as gaming, robotics, and decision optimisation.
• Troubleshoot, visualise, and enhance 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.

Course Customisation Options

• To request a customised version of this course (e.g., using PyTorch instead of TensorFlow), please contact us to make arrangements.

An exploration of artificial intelligence fundamentals reveals how intelligent technology reshapes digital strategy, automation, and decision making across enterprise operations. Examines core concepts spanning AI history, problem-solving frameworks, knowledge representation, uncertain reasoning, and machine learning paradigms alongside communication, perception, and autonomous action. Guides executives and architects 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—specifically Machine Learning and Deep Learning—within the automotive sector. It assists learners in identifying technologies that can be applied across various automotive scenarios, ranging from basic automation and image recognition to complex autonomous decision-making systems.

Artificial Neural Networks serve as a computational data model employed in creating Artificial Intelligence (AI) systems capable of executing tasks considered 'intelligent'. Neural Networks are frequently utilised within Machine Learning (ML) applications, which represent one specific implementation of AI. Deep Learning constitutes a specialised subset of Machine Learning.

This instructor-led, live training in Adelaide (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 Adelaide (online or on-site) is targeted at data scientists who wish to utilise TensorFlow to analyse potential fraud data.

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

• Create a fraud detection model in 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 Adelaide (online or onsite) is tailored for developers and data scientists who wish to utilise 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 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, as well as broader concepts within machine learning algorithms, deep learning frameworks, and their practical applications.

The first part of this training (40%) concentrates on fundamental principles, aiding you in selecting the appropriate technology stack, such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

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

The final portion of the training (40%) focuses extensively on TensorFlow, which is Google's open-source library for deep learning. 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 completion of this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• understand the structure and deployment mechanisms of TensorFlow
• be capable of managing installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production-level tasks such as training models, building graphs, and logging