Deep Learning Training in Christchurch

This instructor-led, live training in Christchurch (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 Christchurch (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 Christchurch (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 Christchurch (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 Christchurch (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 Christchurch (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 Christchurch (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 Christchurch (online or on-site) is designed for biologists who wish to understand how AlphaFold operates and use its models as guides in their experimental research.

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

• Grasp the fundamental principles of AlphaFold.
• Understand how AlphaFold works.
• Learn to interpret AlphaFold predictions and results.

This instructor-led, live training in Christchurch (available online or on-site) is tailored for beginner to intermediate-level data scientists and machine learning engineers seeking to boost the performance of their deep learning models.

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

• Grasp the core principles of distributed deep learning.
• Install and configure DeepSpeed effectively.
• Scale deep learning models across distributed hardware using DeepSpeed.
• Implement and experiment with DeepSpeed features to optimise performance and memory efficiency.

This instructor-led, live training in Christchurch (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 Christchurch, 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.

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.

In this instructor-led, live training in Christchurch, participants will learn how to implement deep learning models for the telecommunications sector using Python, as they progress through the development of a deep learning credit risk model.

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

• Understand the fundamental concepts of deep learning.
• Explore the applications and use cases of deep learning in telecommunications.
• Use Python, Keras, and TensorFlow to build deep learning models tailored for the telecom industry.
• Develop their own deep learning customer churn prediction model using Python.

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 Christchurch (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 Christchurch (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 Christchurch (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 Christchurch (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 course offers a broad overview of Deep Learning without delving too deeply into any specific methods. It is ideal for those who wish to begin using Deep Learning to improve the accuracy of their predictions.

This instructor-led, live training in Christchurch (available online or on-site) is designed for researchers and developers seeking to install, configure, customise, and utilise the DeepMind Lab platform to create general artificial intelligence and machine learning systems.

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

• Customise DeepMind Lab to build and operate an environment tailored to specific learning and training requirements.
• Leverage DeepMind Lab's 3D simulation environment to train learning agents from a first-person perspective.
• Enable agent evaluation to foster intelligence within a 3D game-like world.

Machine learning is a branch of Artificial Intelligence where computers can learn without being explicitly programmed. Deep learning is a subfield of machine learning that employs methods based on learning data representations and structures, such as neural networks.

This instructor-led, live training in Christchurch (online or on-site) is designed for business analysts, data scientists, and developers who aim to build and implement deep learning models to accelerate revenue growth and address real-world business challenges.

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

• Understand the core concepts of machine learning and deep learning.
• Gain insights into the future of business and industry through ML and DL.
• Define business strategies and solutions using deep learning.
• Learn how to apply data science and deep learning to solve business problems.
• Build deep learning models using Python, Pandas, TensorFlow, CNTK, Torch, Keras, and other tools.

In this instructor-led, live training in Christchurch, participants will learn to use Python libraries for NLP by building an application that processes a set of images and generates descriptive captions.

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

• Design and code DL for NLP using Python libraries.
• Create Python code that reads a large collection of images and generates relevant keywords.
• Create Python code that generates captions based on detected keywords.

Audience

This course is designed for deep learning researchers and engineers keen to leverage available tools (primarily open-source) for analysing computer images.

The course includes practical, working examples.

This instructor-led, live training in Christchurch (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

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

• Install the OpenVINO toolkit.
• Accelerate a computer vision application using an FPGA.
• Execute different CNN layers on the FPGA.
• Scale the application across multiple nodes in a Kubernetes cluster.

This instructor-led, live training in Christchurch (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.

This instructor-led, live training in Christchurch (online or on-site) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.

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

• Set up the necessary development environment to start running deep learning trainings.
• Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet.
• Scale deep learning training with Horovod to run on multiple GPUs.

This instructor-led, live training in Christchurch (available online or on-site) is tailored for technical professionals who wish to apply deep learning models to image recognition applications.

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

• Install and configure Keras.
• Rapidly prototype deep learning models.
• Implement a convolutional network.
• Implement a recurrent network.
• Execute a deep learning model on both CPU and GPU.

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 instructor-led, live training in Christchurch (online or on-site) is designed for professionals at beginner to intermediate levels who wish to deepen their understanding of machine learning algorithms, deep learning techniques, and AI-driven decision-making. The course offers hands-on experience with core machine learning concepts, deep learning models, and practical implementations using R.

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

• Grasp the fundamentals of machine learning and deep learning.
• Apply various machine learning algorithms for regression, classification, clustering, and anomaly detection.
• Utilise deep learning architectures such as artificial neural networks (ANNs).
• Implement both supervised and unsupervised learning models.
• Evaluate model performance and optimise hyperparameters.
• Leverage R for data analysis, visualisation, and machine learning applications.

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 instructor-led, live training in Christchurch (delivered online or on-site) is tailored for software engineers who wish to program in Python using OpenCV 4 for deep learning applications.

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

• View, load, and classify images and videos using OpenCV 4.
• Implement deep learning in OpenCV 4 with TensorFlow and Keras.
• Run deep learning models and generate meaningful reports from images and videos.

In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.

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

• Understand and implement unsupervised learning techniques
• Apply clustering and classification to make predictions based on real-world data.
• Visualise data to quickly gain insights, make decisions and further refine analysis.
• Improve the performance of a machine learning model using hyper-parameter tuning.
• Put a model into production for use in a larger application.
• Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.

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

This instructor-led, live training in Christchurch (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 instructor-led, live training in Christchurch (online or onsite) is aimed at engineers who wish to write, load, and run machine learning models on very small embedded devices.

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

• Install TensorFlow Lite.
• Load machine learning models onto an embedded device to enable it to detect speech, classify images, and more.
• Add AI capabilities to hardware devices without relying on network connectivity.

In this instructor-led, live training in Christchurch (available online or on-site), participants will learn how to configure and utilise TensorFlow Serving to deploy and manage ML models in a production setting.

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

• Train, export, and serve a variety of TensorFlow models.
• Test and deploy algorithms using a unified architecture and API set.
• Extend TensorFlow Serving to support other model types beyond TensorFlow.

TensorFlow serves as the second-generation API within Google's open-source software library for Deep Learning. This system is engineered to support machine learning research, enabling a swift and straightforward transition from research prototypes to robust production systems.

Audience

This course is designed for engineers looking to leverage TensorFlow for their Deep Learning initiatives.

Upon completion of this course, delegates will be able to:

• grasp the structure and deployment mechanisms of TensorFlow
• perform installation, configure production environments, and manage architectural tasks
• assess code quality, as well as carry out debugging and monitoring
• implement advanced production-level tasks such as training models, constructing graphs, and logging

This course explores, through specific examples, the application of TensorFlow for image recognition purposes

Audience

This course is intended for engineers seeking to leverage TensorFlow for image recognition tasks

Upon completing this course, delegates will be able to:

• understand TensorFlow's architecture and deployment mechanisms
• perform installation, production environment setup, and architecture configuration
• assess code quality, conduct debugging, and implement monitoring
• execute advanced production tasks such as model training, graph construction, and logging

This instructor-led, live training in Christchurch (online or on-site) is designed for data scientists who want to progress from training a single ML model to deploying multiple ML models into production.

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

• Install and configure TFX and supporting third-party tools.
• Use TFX to create and manage a complete ML production pipeline.
• Work with TFX components to perform modelling, training, inference serving, and deployment management.
• Deploy machine learning features to web applications, mobile applications, IoT devices, and more.

TensorFlow™ is an open-source software library for numerical computation using data flow graphs.

SyntaxNet is a neural-network-based Natural Language Processing framework built for TensorFlow.

Word2Vec is used to learn vector representations of words, known as "word embeddings". Word2Vec is a highly computationally efficient predictive model for learning word embeddings from raw text. It is available in two variants: the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (as described in Chapters 3.1 and 3.2 of Mikolov et al.).

When used together, SyntaxNet and Word2Vec enable users to generate learned embedding models from natural language input.

Audience

This course is designed for developers and engineers who plan to work with SyntaxNet and Word2Vec models within their TensorFlow graphs.

Upon completing this course, delegates will be able to:

• understand TensorFlow's structure and deployment mechanisms
• carry out installation, production environment setup, architecture tasks, and configuration
• assess code quality, perform debugging and monitoring
• implement advanced production techniques such as training models, embedding terms, building graphs, and logging

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