TensorFlow Training in Christchurch

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 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.

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 (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 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.

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