Online TensorFlow Training in the UAE

This instructor-led live training in the UAE (online or onsite) is designed for advanced professionals aiming to deepen their grasp of computer vision and explore TensorFlow’s capabilities for building advanced vision models using Google Colab.

By the conclusion 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 the UAE (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 intensive four-day program introduces the fundamentals of artificial intelligence and its practical applications. Upon completion, participants have the option to extend their learning by dedicating an additional day to executing a hands-on AI project.

In this instructor-led, live training in the UAE, 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

Designed for researchers and engineers in Deep Learning who wish to leverage accessible tools (primarily open-source solutions) to analyze digital imagery.

The course includes practical, hands-on examples.

This instructor-led live training in the UAE (online or onsite) is tailored for data scientists aiming to apply TensorFlow to the analysis of potential fraud data.

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

• Construct a fraud detection model using Python and TensorFlow.
• Build linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

This instructor-led, live training in the UAE (online or on-site) is designed for developers and data scientists who wish to leverage 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 the UAE (online or onsite), participants will learn to configure and use TensorFlow Serving for deploying and managing 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 serves as a second-generation API within Google's open-source library for Deep Learning. This system is engineered to streamline machine learning research, enabling a swift and seamless transition from research prototypes to production environments.

Target Audience

This course is designed for engineers who aim to leverage TensorFlow for their Deep Learning initiatives.

Upon completing this course, participants will be able to:

• Comprehend the structure and deployment mechanisms of TensorFlow.
• Execute tasks related to installation, production environment setup, and architectural configuration.
• Evaluate code quality, perform debugging, and monitor system performance.
• Implement advanced production-level practices, such as training models, constructing graphs, and setting up logging.

This course examines, through concrete examples, how TensorFlow is applied to image recognition tasks.

Audience

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

Upon completing this course, participants will be able to:

• Comprehend the structure and deployment mechanisms of TensorFlow
• Execute installation, production environment setup, architecture tasks, and configuration
• Assess code quality, perform debugging, and conduct monitoring
• Implement advanced production practices such as model training, graph construction, and logging

This instructor-led, live training in the UAE (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™ serves as an open-source software library designed for numerical computations via data flow graphs.

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

Word2Vec is employed to learn vector representations of words, known as "word embeddings." It is a highly computationally efficient predictive model for deriving these embeddings from raw text. It offers two primary architectures: 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 generate learned embedding models from natural language inputs.

Target Audience

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

Upon completing this course, participants will be able to:

• comprehend TensorFlow’s structural framework and deployment mechanisms
• execute installation, production environment setup, and architectural configuration tasks
• assess code quality, and perform debugging and monitoring
• implement advanced production-ready features such as model training, term embedding, graph construction, and logging

This course provides a comprehensive conceptual foundation in neural networks, machine learning algorithms, and deep learning applications.

Part 1 (40%) of the training focuses on fundamental principles, equipping you to select the most appropriate technologies for your needs, such as TensorFlow, Caffe, Theano, DeepDrive, and Keras.

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

Part 3 (40%) is heavily centered on TensorFlow, Google's open-source library for deep learning. All examples and hands-on exercises in this section are conducted within the TensorFlow environment.

Audience

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

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

• Demonstrate a solid understanding of Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN).
• Comprehend TensorFlow's architecture and deployment mechanisms.
• Perform installation tasks, configure production environments, and manage system architecture.
• Assess code quality, and execute debugging and monitoring procedures.
• Implement advanced production workflows, including model training, graph construction, and logging.