Online TensorFlow Training in New Zealand

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.

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 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