Deep Learning with TensorFlow Training Course

This four-day course provides an introduction to AI and its applications. Upon completion, there is an option to extend the course by one additional day to undertake a dedicated AI project.

This instructor-led, live training in South Korea (online or onsite) is designed for advanced-level professionals seeking 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 South Korea (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.

In this instructor-led live training in South Korea, participants will learn how to use Python libraries for NLP by building an application that processes a set of images and generates captions.

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

• Design and implement NLP Deep Learning solutions using Python libraries.
• Write Python code to read large volumes of images and extract keywords.
• Develop Python code that generates captions based on the identified keywords.

Target Audience

This course is designed for researchers and engineers in the field of deep learning who wish to leverage available tools, primarily open-source solutions, for the analysis of visual data.

The curriculum includes practical, working examples.

This instructor-led, live training (online or onsite) is designed for data scientists who want 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 South Korea (online or onsite) is designed for developers and data scientists who want 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 previous versions.
• Construct deep learning models.
• Implement an advanced image classifier.
• Deploy deep learning models to the cloud, mobile devices, and IoT devices.

In this instructor-led live training South Korea (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.

This course examines the application of TensorFlow to image recognition through concrete examples.

Audience

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

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

• comprehend TensorFlow’s architecture and deployment mechanisms
• execute installation, production environment setup, and architecture configuration
• evaluate code quality, conduct debugging, and perform monitoring
• implement advanced production workflows such as training models, constructing graphs, and logging

This instructor-led live training in South Korea (online or onsite) is aimed at data scientists who wish to move from training single ML models to deploying multiple 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 via data flow graphs.

SyntaxNet serves as a neural-network-based NLP framework built for TensorFlow.

Word2Vec is utilized to learn vector representations of words, known as "word embeddings." It is a highly computationally efficient predictive model for learning these embeddings from raw text. It offers two variants: 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 using SyntaxNet and Word2Vec together, 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.

After completing this course, delegates will:

• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and conduct monitoring
• be able to implement advanced production-level tasks such as training models, embedding terms, building graphs, and logging

This course provides foundational knowledge of neural networks and their broader role within machine learning and deep learning algorithms and applications.

The first segment (40%) of the training emphasizes core fundamentals, equipping you to select appropriate technologies such as TensorFlow, Caffe, Theano, DeepDrive, and Keras.

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

The third segment (40%) focuses extensively on TensorFlow, the open-source software library for deep learning developed by Google. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

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

Upon completion, participants will:

• gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend the structure and deployment mechanisms of TensorFlow
• possess the skills to manage installation, production environment setup, architecture tasks, and configuration
• be capable of assessing code quality, performing debugging, and monitoring systems
• be able to implement advanced production-level tasks, including model training, graph construction, and logging