TensorFlow Extended (TFX) Training Course

This four-day course provides an introduction to artificial intelligence and its practical applications. Upon completing the course, participants have the option to dedicate an additional day to working on an AI project.

This instructor-led, live training in Taiwan (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 instructor-led, live training in Taiwan (online or onsite) is designed for intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

Upon completing this training, participants will be able to:

• Set up and navigate Google Colab for deep learning projects.
• Grasp the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Leverage advanced TensorFlow features for deep learning.

In this instructor-led, live training in Taiwan, 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.

Target Audience

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

The course offers practical, working examples.

This instructor-led, live training in Taiwan (online or onsite) is designed for data scientists who wish to utilize TensorFlow for analyzing potential fraud data.

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

• Create a fraud detection model using 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 Taiwan (online or onsite) is designed for developers and data scientists who wish to utilize TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and more.

By the conclusion 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 conducted in Taiwan (online or onsite), participants will learn how to configure and utilize 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 support other types of models beyond TensorFlow models.

TensorFlow is the second-generation API of Google's open-source software library for Deep Learning. This system is designed to facilitate machine learning research, enabling developers to quickly and easily transition from research prototypes to production systems.

Audience

This course is intended for engineers who wish to leverage TensorFlow for their Deep Learning projects.

After completing this course, delegates will:

• understand TensorFlow's structure and deployment mechanisms
• be able to carry out installation, production environment, architecture tasks and configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production-like training models, building graphs and logging

This course delves into the practical application of TensorFlow for image recognition, illustrated with 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 TensorFlow’s architecture and deployment mechanisms
• Execute installation, production environment setup, architectural design, and configuration
• Evaluate code quality, and conduct debugging and monitoring
• Implement advanced production workflows, such as model training, graph construction, and 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 for TensorFlow.

Word2Vec is utilized for learning vector representations of words, known as "word embeddings." Word2vec offers a particularly computationally efficient predictive model for learning these embeddings from raw text. It is available in two variations: the Continuous Bag-of-Words (CBOW) model and the Skip-Gram model (refer to Chapters 3.1 and 3.2 in Mikolov et al.).

By employing SyntaxNet and Word2Vec in tandem, users can generate 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.

Upon completion of this course, participants will:

• gain a clear understanding of TensorFlow’s structure and deployment mechanisms
• be capable of performing installation, production environment setup, architectural tasks, and configuration
• be able to evaluate code quality, conduct debugging, and implement monitoring
• know how to implement advanced production tasks such as training models, embedding terms, constructing graphs, and logging

This course provides foundational knowledge of neural networks and broader machine learning algorithms, with a specific focus on deep learning techniques and their practical applications.

The first section (40% of the curriculum) emphasizes core fundamentals, equipping you with the insights needed to select the most appropriate technology for your needs, such as TensorFlow, Caffe, Theano, DeepDrive, or Keras.

The second section (20% of the curriculum) introduces Theano, a Python library designed to simplify the creation of deep learning models.

The final section (40% of the curriculum) focuses extensively on TensorFlow, Google’s open-source software library for deep learning. All examples and hands-on exercises will be conducted within the TensorFlow framework.

Audience

This course is designed for engineers looking to leverage TensorFlow for their deep learning projects.

Upon completion, participants will:

• Possess a solid understanding of Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs)
• Understand the architecture and deployment mechanisms of TensorFlow
• Be capable of handling installation, production environment setup, and architectural configuration tasks
• Assess code quality, perform debugging, and implement monitoring systems
• Implement advanced production-level tasks such as training models, constructing computational graphs, and setting up logging