Online TensorFlow Training in the UAE

This instructor-led, live training in the UAE (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 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 four-day course provides an introduction to artificial intelligence and its applications. Participants also have the option to extend their learning with an extra day dedicated to working on an AI project upon finishing the course.

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.

Audience

This course is ideal for Deep Learning researchers and engineers who wish to leverage existing tools, primarily open-source, for the analysis of digital imagery.

The course includes practical examples.

This instructor-led, live training in the UAE (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 the UAE (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 the UAE (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 an open-source software library for Deep Learning developed by Google. It serves as a second-generation API aimed at simplifying machine learning research while enabling swift transitions from prototypes to operational systems.

Audience

This course targets engineers who wish to leverage TensorFlow in their Deep Learning initiatives.

Upon completion of this course, participants will:

• grasp the architecture and deployment strategies of TensorFlow
• be proficient in installation, environment setup, architectural tasks, and configuration
• possess the skills to evaluate code quality, debug, and monitor systems
• master advanced production techniques such as training models, constructing graphs, and logging data

This course delves into the practical application of TensorFlow for image recognition through specific examples.

Audience

The course is designed for engineers aiming to leverage TensorFlow for image recognition tasks.

Upon completion, participants will be able to:

• grasp the structure and deployment methods of TensorFlow
• execute installation, production environment setup, architecture tasks, and configurations
• evaluate code quality, conduct debugging, and monitor performance
• implement advanced production techniques such as training models, constructing graphs, and logging data

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.

In this instructor-led, live training in the UAE, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.

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

• Train various types of neural networks on large amounts of data.
• Use TPUs to speed up the inference process by up to two orders of magnitude.
• Utilize TPUs to process intensive applications such as image search, cloud vision and photos.

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 that operates within TensorFlow.

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

When used together, SyntaxNet and Word2Vec enable users to generate 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.

Upon completion of this course, participants will:

• grasp the structure and deployment methods of TensorFlow
• be capable of performing installation, setting up production environments, configuring architecture tasks
• assess code quality, conduct debugging, and monitoring
• implement advanced production techniques such as training models, embedding terms, constructing graphs, and logging

This course starts by providing you with a conceptual understanding of neural networks and machine learning algorithms, including deep learning (algorithms and applications).

The first part (40%) of this training focuses on fundamentals but will assist you in selecting the appropriate technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

The second part (20%) introduces Theano—a Python library that simplifies writing deep learning models.

The third part (40%) of the training is heavily based on TensorFlow—the second-generation API of Google's open-source software library for deep learning. All examples and hands-on exercises will be conducted using TensorFlow.

Target Audience

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

Upon completion of this course, participants will:

• have a solid understanding of deep neural networks (DNN), CNN, and RNN

• understand the structure and deployment mechanisms of TensorFlow

• be capable of performing installation, setting up production environments, configuring architecture tasks

• assess code quality, perform debugging, and monitoring

• implement advanced production tasks such as training models, building graphs, and logging