Deep Learning Training in the UAE

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

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

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

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 instructor-led, live training in the UAE (online or onsite) is aimed at advanced-level professionals who wish to specialize in cutting-edge deep learning techniques for NLU.

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

• Understand the key differences between NLU and NLP models.
• Apply advanced deep learning techniques to NLU tasks.
• Explore deep architectures such as transformers and attention mechanisms.
• Leverage future trends in NLU for building sophisticated AI systems.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

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

• Understand advanced deep learning architectures and techniques for text-to-image generation.
• Implement complex models and optimizations for high-quality image synthesis.
• Optimize performance and scalability for large datasets and complex models.
• Tune hyperparameters for better model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools

This instructor-led, live training in the UAE (online or onsite) is aimed at advanced-level professionals who wish to leverage AI techniques to revolutionize drug discovery and development processes.

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

• Understand the role of AI in drug discovery and development.
• Apply machine learning techniques to predict molecular properties and interactions.
• Use deep learning models for virtual screening and lead optimization.
• Integrate AI-driven approaches into the clinical trial process.

This instructor-led, live training in the UAE (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

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

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

This instructor-led, live training in the UAE (online or onsite) is aimed at beginner to intermediate-level data scientists and machine learning engineers who wish to improve the performance of their deep learning models.

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

• Understand the principles of distributed deep learning.
• Install and configure DeepSpeed.
• Scale deep learning models on distributed hardware using DeepSpeed.
• Implement and experiment with DeepSpeed features for optimization and memory efficiency.

This instructor-led, live training in the UAE (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.

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

• Set up a development environment that includes a popular LLM.
• Create a basic LLM and fine-tune it on a custom dataset.
• Use LLMs for different natural language tasks such as text summarization, question answering, text generation, and more.
• Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.

This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

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

• Understand the principles of Stable Diffusion and how it works for image generation.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

In this instructor-led, live training in the UAE, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.

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

• Implement machine learning algorithms and techniques for solving complex problems.
• Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
• Push Python algorithms to their maximum potential.
• Use libraries and packages such as NumPy and Theano.

This instructor-led, live training in the UAE (online or onsite) is aimed at developers and data scientists who wish to learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.

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

• Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning.
• Apply advanced Reinforcement Learning algorithms to solve real-world problems.
• Build a Deep Learning Agent.

In this instructor-led, live training in the UAE, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model.

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

• Understand the fundamental concepts of deep learning.
• Learn the applications and uses of deep learning in telecom.
• Use Python, Keras, and TensorFlow to create deep learning models for telecom.
• Build their own deep learning customer churn prediction model using Python.

This course is designed for managers, solutions architects, innovation officers, CTOs, software architects, and anyone else interested in gaining an understanding of practical artificial intelligence applications and predictions for its future growth.

This course delves into AI, with a focus on Machine Learning and Deep Learning, within the context of the automotive industry. It aids in identifying which technologies could potentially be applied across various scenarios in a vehicle, ranging from basic automation and image recognition to advanced autonomous decision-making.

An Artificial Neural Network is a computational model utilized in the creation of Artificial Intelligence (AI) systems designed to execute "intelligent" tasks. These networks are frequently employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning falls under the broader category of Machine 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.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level data scientists and statisticians who wish to prepare data, build models, and apply machine learning techniques effectively in their professional domains.

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

• Understand and implement various Machine Learning algorithms.
• Prepare data and models for machine learning applications.
• Conduct post hoc analyses and visualize results effectively.
• Apply machine learning techniques to real-world, sector-specific scenarios.

Caffe is a deep learning framework designed with emphasis on expressiveness, speed, and modularity.

This course delves into the application of Caffe as a deep learning tool for image recognition, using MNIST as an illustrative example.

Audience

This course is ideal for deep learning researchers and engineers who wish to leverage Caffe as their framework.

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

• grasp the structure and deployment methods of Caffe
• execute installation tasks, manage production environments, and configure architecture
• evaluate code quality, conduct debugging, and monitor performance
• implement advanced functionalities such as training models, creating layers, and logging

This instructor-led, live training in the UAE (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.

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

• Set up the necessary development environment to start developing neural network models.
• Define and implement neural network models using a comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.

The Computer Network ToolKit (CNTK) is an open-source, multi-machine, multi-GPU machine learning framework developed by Microsoft. It specializes in efficient RNN training for applications involving speech, text, and images.

Audience

This course targets engineers and architects who wish to integrate CNTK into their projects.

This course provides a broad introduction to Deep Learning without delving deeply into particular techniques. It is ideal for individuals looking to utilize Deep Learning to improve the precision of their predictions.

This instructor-led, live training in the UAE (online or onsite) is aimed at researchers and developers who wish to install, set up, customize, and use the DeepMind Lab platform to develop general artificial intelligence and machine learning systems.

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

• Customize DeepMind Lab to build and run an environment that suits learning and training needs.
• Use DeepMind Lab's 3D simulation environment to train learning agents in a first-person viewpoint.
• Facilitate agent evaluation to develop intelligence in a 3D game-like world.

Machine learning is a segment of Artificial Intelligence that enables computers to acquire knowledge without explicit programming. Deep learning, a subset of machine learning, employs techniques centered around data representation and structures like neural networks.

This instructor-led, live training in the UAE (online or onsite) is aimed at business analysts, data scientists, and developers who wish to build and implement deep learning models to accelerate revenue growth and solve problems in the business world.

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

• Understand the core concepts of machine learning and deep learning.
• Get insights on the future of business and industry with ML and DL.
• Define business strategies and solutions with deep learning.
• Learn how to apply data science and deep learning in solving business problems.
• Build deep learning models using Python, Pandas, TensorFlow, CNTK, Torch, Keras, etc.

Machine learning is a subset of Artificial Intelligence that enables computers to learn without explicit programming. Deep learning, a specialized area within machine learning, employs techniques based on data representation and structures such as neural networks. Python is a high-level programming language renowned for its clear syntax and easy-to-read code.

In this instructor-led live training session, participants will gain hands-on experience in developing deep learning models for the banking sector by building a deep learning credit risk model step-by-step using Python.

By the conclusion of this training, attendees will be able to:

• Grasp the core principles of deep learning
• Explore the applications and benefits of deep learning in banking
• Utilize Python, Keras, and TensorFlow to construct deep learning models for banking purposes
• Create their own deep learning credit risk model using Python

Audience

• Software Developers
• Data Scientists

Course Format

• The course will consist of lectures, discussions, exercises, and extensive practical sessions.

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 accelerate real-time machine learning applications and deploy them at scale.

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

• Install the OpenVINO toolkit.
• Accelerate a computer vision application using an FPGA.
• Execute different CNN layers on the FPGA.
• Scale the application across multiple nodes in a Kubernetes cluster.

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 or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.

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

• Set up the necessary development environment to start running deep learning trainings.
• Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet.
• Scale deep learning training with Horovod to run on multiple GPUs.

Artificial intelligence has transformed numerous economic sectors such as industry, medicine, and communication, following its significant impact on many scientific domains. However, its portrayal in mainstream media often leans towards fantasy, diverging from the realities of Machine Learning and Deep Learning. This course aims to introduce engineers with a master's degree in computing tools—including foundational software programming skills—to Deep Learning and its various specialized areas, as well as current primary network architectures. While mathematical fundamentals will be reviewed during the course, a math proficiency equivalent to two years post-secondary education is recommended for greater ease of understanding. It is possible to overlook the mathematical aspects to maintain a systems-level perspective; however, this approach will significantly restrict your comprehension of the subject matter.

This instructor-led, live training in the UAE (online or onsite) is aimed at technical persons who wish to apply deep learning model to image recognition applications.

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

• Install and configure Keras.
• Quickly prototype deep learning models.
• Implement a convolutional network.
• Implement a recurrent network.
• Execute a deep learning model on both a CPU and GPU.

In this instructor-led live training session, participants will gain skills in utilizing Matlab to create, construct, and visualize a convolutional neural network for image recognition.

Upon completion of the course, participants will be equipped with the ability to:

• Create a deep learning model
• Automate the process of data labeling
• Operate with models from Caffe and TensorFlow-Keras
• Train datasets using multiple GPUs, cloud services, or clusters

Audience

• Software Developers
• Engineers
• Subject Matter Experts

Course Format

• The course includes lectures, discussions, practical exercises, and extensive hands-on practice.

This instructor-led, live training in the UAE (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to develop their understanding of machine learning algorithms, deep learning techniques, and AI-driven decision-making. The course provides hands-on experience with machine learning concepts, deep learning models, and practical implementations using R.

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

• Understand the fundamentals of machine learning and deep learning.
• Apply various machine learning algorithms for regression, classification, clustering, and anomaly detection.
• Use deep learning architectures such as artificial neural networks (ANNs).
• Implement supervised and unsupervised learning models.
• Evaluate model performance and optimize hyperparameters.
• Use R for data analysis, visualization, and machine learning applications.

This training is designed for individuals interested in mastering the fundamentals of neural networks and exploring their various applications.

This classroom-based training session will include presentations and computer-based examples, along with case study exercises that utilize pertinent neural and deep learning network libraries.

This instructor-led, live training in the UAE (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.

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

• View, load, and classify images and videos using OpenCV 4.
• Implement deep learning in OpenCV 4 with TensorFlow and Keras.
• Run deep learning models and generate impactful reports from images and videos.

OpenFace is an open-source facial recognition software that operates in real-time and is built using Python and Torch, based on Google's FaceNet research.

In this instructor-led live training session, participants will learn how to utilize OpenFace’s components to develop and deploy a sample facial recognition application.

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

• Work with OpenFace’s components such as dlib, OpenCV, Torch, and nn4 to implement face detection, alignment, and transformation
• Apply OpenFace in practical applications like surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, among others.

Audience

• Developers
• Data scientists

Course Format

• The course includes lectures, discussions, exercises, and extensive hands-on practice.

In this instructor-led live training session, participants will learn to configure and utilize OpenNMT for translating various sample datasets. The course begins with an introduction to neural networks in the context of machine translation. Throughout the course, attendees will engage in practical exercises to reinforce their understanding and receive feedback from the instructor.

Upon completion of this training, participants will possess both the knowledge and hands-on experience required to deploy a live OpenNMT solution.

The source and target language samples will be predetermined based on the audience's specific needs.

Course Format

• A blend of lectures, discussions, and extensive practical exercises

In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.

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

• Understand and implement unsupervised learning techniques
• Apply clustering and classification to make predictions based on real world data.
• Visualize data to quicly gain insights, make decisions and further refine analysis.
• Improve the performance of a machine learning model using hyper-parameter tuning.
• Put a model into production for use in a larger application.
• Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.

This course provides an overview of utilizing neural networks to solve practical problems through the use of R-project software.

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.

This instructor-led, live training in the UAE (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

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

• Install TensorFlow Lite.
• Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
• Add AI to hardware devices without relying on network connectivity.

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