Deep Learning with TensorFlow Training Course

This four-day programme provides an introduction to artificial intelligence and its practical applications. Participants may also opt to extend the course by one day to work on a dedicated AI project upon completion.

This instructor-led, live training in Malaysia (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 Malaysia (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 Malaysia, participants will learn to utilise Python libraries for NLP by creating an application that processes a set of images 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—for the analysis of computer images.

Participants will gain exposure to practical, working examples.

This instructor-led live training in Malaysia (online or on-site) is designed for data scientists who wish to utilise TensorFlow to analyse potential fraud data.

By the conclusion 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 analysing fraud data.

This instructor-led live training in Malaysia (online or onsite) targets developers and data scientists who intend to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and other applications.

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 at Malaysia (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, through practical examples, how TensorFlow is applied to image recognition tasks.

Audience

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

Upon completing this course, participants will be able to:

• gain a clear understanding of TensorFlow's structure and deployment mechanisms
• execute installation tasks, configure production environments, and design architectures
• evaluate code quality, perform debugging, and set up monitoring
• implement advanced production practices such as training models, constructing graphs, and logging

This instructor-led, live training in Malaysia (online or onsite) targets data scientists who wish to progress from training a single ML model to deploying numerous 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 carry out modelling, 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 for TensorFlow.

Word2Vec facilitates the learning of vector representations for words, known as "word embeddings". It is a highly computationally efficient predictive model for acquiring these embeddings from raw text. It features 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.).

By employing SyntaxNet alongside Word2Vec, users can generate learned embedding models derived 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 be able to:

• Comprehend TensorFlow’s architecture and deployment mechanisms
• Execute installation, production environment setup, architectural tasks, and configuration
• Evaluate code quality, and perform debugging and monitoring
• Implement advanced production-level tasks such as training models, embedding terms, building graphs, and logging

This course provides foundational knowledge on neural networks, machine learning algorithms, and deep learning concepts and applications.

The first part (40%) of this training focuses on fundamentals, helping you select the appropriate technology such as TensorFlow, Caffe, Theano, DeepDrive, and Keras.

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

The third part (40%) is heavily based 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 who wish to utilise TensorFlow for their Deep Learning projects.

Upon completing this course, delegates will be able to:

• gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend TensorFlow’s structure and deployment mechanisms
• perform installation, production environment setup, architecture tasks, and configuration
• assess code quality, debug, and monitor systems
• implement advanced production-level tasks such as training models, constructing graphs, and logging