Deep Learning for Business Training Course

This instructor-led, live training in New Zealand (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 New Zealand (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 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 New Zealand (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 New Zealand (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 New Zealand (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.

In this instructor-led, live training in New Zealand, 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 New Zealand (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 New Zealand (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 New Zealand (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 New Zealand (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 New Zealand (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.

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.

This instructor-led, live training in New Zealand (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.