TensorFlow Training in Brisbane

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

This instructor-led live training in Brisbane (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