Online TensorFlow Training in Taiwan

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 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.
• Utilize advanced features of TensorFlow for deep learning.

This is a four-day course that provides an introduction to AI and its applications. Participants have the option to extend their learning with an additional day dedicated to working on an AI project upon completing the course.

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.

Audience

This course is designed for deep learning researchers and engineers who are keen on leveraging existing tools, primarily open-source, to analyze computer images.

The course includes practical examples to help you get started.

This instructor-led, live training in Taiwan (online or onsite) is aimed at data scientists who wish 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 Taiwan (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.x to build predictors, classifiers, generative models, neural networks and so on.

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 Taiwan (online or onsite), participants will learn how 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 serve other types of models beyond TensorFlow models.

TensorFlow is the second-generation API of Google's open-source software library for deep learning. It is designed to support research in machine learning and streamline the transition from research prototypes to production systems.

Audience

This course is tailored for engineers who are looking to utilize TensorFlow for their deep learning projects.

After completing this course, participants will:

• understand the structure and deployment mechanisms of TensorFlow
• be capable of performing installation, production environment setup, architectural tasks, and configuration
• be able to evaluate code quality, conduct debugging, and perform monitoring
• be equipped to implement advanced production tasks such as training models, constructing graphs, and logging

This course delves into the practical application of TensorFlow for image recognition, using specific examples to illustrate key concepts.

Audience

This course is designed for engineers who aim to leverage TensorFlow for image recognition tasks.

After completing this course, delegates will be able to:

• comprehend TensorFlow’s architecture and deployment processes
• execute installation, production environment setup, architectural tasks, and configuration
• evaluate code quality, perform debugging, and monitoring
• implement advanced production techniques such as training models, building graphs, and logging

This instructor-led, live training in Taiwan (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to 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 carry out modeling, training, serving inference, and managing deployments.
• Deploy machine learning features to web applications, mobile applications, IoT devices and more.

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

SyntaxNet is a neural-network framework for Natural Language Processing built on TensorFlow.

Word2Vec is utilized for learning vector representations of words, known as "word embeddings." Word2vec is a highly computationally efficient predictive model for generating word embeddings from raw text. It offers two variants: the Continuous Bag-of-Words (CBOW) model and the Skip-Gram model (as described in Chapter 3.1 and 3.2 of Mikolov et al.).

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

Audience

This course is aimed at Developers and engineers who plan to work with SyntaxNet and Word2Vec models within their TensorFlow graphs.

After completing this course, delegates will:

• understand the structure and deployment mechanisms of TensorFlow
• be capable of performing installation, production environment setup, architectural tasks, and configuration
• be able to evaluate code quality, perform debugging, and monitoring
• be proficient in implementing advanced production tasks such as training models, embedding terms, building graphs, and logging

This course starts by providing you with a solid foundation in neural networks and the broader field of machine learning algorithms, including deep learning (both algorithms and applications).

The first part of the training (40%) focuses on the fundamentals, helping you choose the right technology from options such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

The second part of the training (20%) introduces Theano, a Python library that simplifies the process of writing deep learning models.

The third part of the training (40%) delves deeply into TensorFlow, Google's open-source software library for Deep Learning. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers who want to use TensorFlow in their deep learning projects.

After completing this course, participants will:

• have a strong understanding of deep neural networks (DNN), convolutional neural networks (CNN), and recurrent neural networks (RNN)
• understand TensorFlow’s architecture and deployment mechanisms
• be capable of performing installation, setting up production environments, and configuring architectures
• be able to evaluate code quality, perform debugging, and monitor performance
• be proficient in advanced tasks such as training models, constructing graphs, and logging activities