TensorFlow Extended (TFX) Training Course

Spanning four days, this course provides a foundational introduction to artificial intelligence and its practical applications. Participants may also choose to extend their learning by dedicating an additional day to working on a real-world AI project upon completing the course.

This instructor-led, live training in Norway (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.
• Visualize and interpret the results of image classification models.

This live, instructor-led training in Norway (online or onsite) targets intermediate-level data scientists and developers eager to understand and apply deep learning techniques within the Google Colab ecosystem.

By the conclusion 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.

In this instructor-led, live training in Norway, participants will learn to use Python libraries for NLP as they create an application that processes a set of pictures and generates 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 substantially huge collection of pictures and generates keywords.
• Create Python Code that generates captions from the detected keywords.

Audience

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

The course includes practical working examples.

This instructor-led live training in Norway (online or on-site) targets data scientists who intend to use TensorFlow to analyze 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 analyzing fraud data.

This instructor-led, live training in Norway (online or onsite) 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 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.

In this instructor-led live training in Norway (online or onsite), participants will learn to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.

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

• Train, export, and serve various TensorFlow models.
• Test and deploy algorithms using a single architecture and set of APIs.
• Extend TensorFlow Serving to accommodate other types of models beyond TensorFlow.

TensorFlow is a second-generation API within Google’s open-source library for Deep Learning. The framework is engineered to support machine learning research, enabling developers to swiftly move from research prototypes to production-ready systems.

Audience

This course is designed for engineers who wish to apply TensorFlow to their Deep Learning initiatives.

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

• Comprehend TensorFlow’s structure and deployment mechanisms
• Perform installation, set up production environments, configure architecture, and manage settings
• Evaluate code quality, debug issues, and monitor performance
• Implement advanced production-level tasks such as training models, constructing graphs, and logging data

This course delves into the practical application of TensorFlow for image recognition, illustrated through concrete examples.

Target Audience

The course is designed for engineers who wish to leverage TensorFlow for image recognition tasks.

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

• Grasp the underlying structure and deployment mechanisms of TensorFlow
• Execute installation, production environment setup, and architectural configuration
• Evaluate code quality, and perform debugging and monitoring
• Implement advanced production-level tasks such as training models, constructing graphs, and managing logging

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

SyntaxNet serves as a neural-network-based Natural Language Processing framework tailored for TensorFlow.

Word2Vec is utilized for learning vector representations of words, known as "word embeddings." This predictive model is particularly computationally efficient for deriving word embeddings from raw text. It offers two variations: the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (refer to Chapters 3.1 and 3.2 in Mikolov et al.).

When used together, SyntaxNet and Word2Vec enable users to create Learned Embedding models from Natural Language input.

Audience

This course is designed for developers and engineers who plan to integrate SyntaxNet and Word2Vec models into their TensorFlow graphs.

After completing this course, participants will be able to:

• grasp the architecture and deployment mechanisms of TensorFlow
• execute installation, production environment setup, and architectural configuration tasks
• assess code quality, perform debugging, and monitor systems
• implement advanced production-level activities such as training models, embedding terms, constructing graphs, and logging

This course initiates with a conceptual foundation in neural networks, covering broad aspects of machine learning algorithms and deep learning (including algorithms and their applications).

Part-1 (40%) of this training concentrates on fundamentals, aiding you in selecting the appropriate technology such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

Part-2 (20%) introduces Theano, a Python library designed to simplify the creation of deep learning models.

Part-3 (40%) of the training is heavily focused on TensorFlow, the API for Google's open-source deep learning software library. All examples and hands-on exercises will utilize TensorFlow.

Audience

This course is designed for engineers aiming to utilize TensorFlow for their deep learning projects.

Upon completing this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• understand TensorFlow's structure and deployment mechanisms
• be capable of handling installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitor systems
• be able to implement advanced production tasks such as training models, building graphs, and logging