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Deep Learning Course in Dubai

A professional deep learning concept with a person working on a laptop and a glowing digital brain graphic symbolizing advanced AI technology.
Edoxi’s 40-hour KHDA-approved Deep Learning course in Dubai equips you with practical, hands-on skills to master neural network architectures and build advanced AI systems. Learn TensorFlow and PyTorch for image, text, and data analysis, with extensive lab sessions and real-world projects. Advance your career in AI, computer vision, and NLP. Enrol now to enhance your expertise and position yourself for Dubai’s high-demand roles.
Course Duration
40 Hours
Corporate Days
5 Days
Learners Enrolled
50+
Modules
5
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Course Rating
4.9
star-rating-4.9
Mode of Delivery
Online
Classroom
Certification by

What Do You Learn from Edoxi's Deep Learning Training

Deep Learning Foundations
Understand core mathematical principles behind neural networks and backpropagation. Implement gradient descent variants and regularisation techniques for optimal learning.
CNN, RNN, and ANN Architectures
Learn different neural network types for specific applications. Implement these architectures using TensorFlow and PyTorch frameworks.
Computer Vision Applications
Develop image classification and object detection systems using convolutional networks. Apply transfer learning with pre-trained models for visual tasks.
Deep Architecture Design
Build multi-layer networks with proper weight initialisation and connectivity patterns. Design effective architectures for complex learning tasks.
Activation Functions and Optimisation
Apply appropriate activation functions to improve model performance. Understand when to use sigmoid, ReLU, tanh and advanced variants.
Model Evaluation Techniques
Implement comprehensive evaluation metrics to assess model performance. Learn techniques for validation, testing, and performance optimisation.

About Our Deep Learning Course in Dubai

Edoxi’s 40-hour KHDA-approved Deep Learning course in Dubai equips data scientists, developers, and AI enthusiasts with strong expertise in neural network architectures. It builds practical skills to create and deploy real-world deep learning models used across Dubai’s growing tech sector. The course is available in classroom, online, and corporate formats for learners aiming to develop advanced AI systems for image, text, and predictive analytics.

The Deep Learning course curriculum covers core deep learning foundations, including neural network principles, various architectures like CNN, RNN, and ANN, and advanced techniques such as optimisation and regularisation. Through extensive hands-on labs with TensorFlow and PyTorch, learners gain practical experience in developing image classification, object detection, and sequence modelling applications. The Deep Learning training concludes in a capstone project that applies these skills to real-world data.

Upon completing our Deep Learning course, you can earn an Edoxi Course Completion Certificate that validates your expertise in deep learning. You also gain the skills required for Dubai’s high-demand job roles, such as Deep Learning Engineer, Computer Vision Engineer, and AI Researcher.

Enrol now to advance your career in Dubai and make an impact in the field of Artificial Intelligence.

Key Features of Edoxi's Deep Learning Training

Hands-on TensorFlow & PyTorch Labs

Access comprehensive lab environments for building neural networks and implementing deep learning models with industry-standard frameworks.

Complete Deep Learning Project Portfolio

Develop a professional portfolio through hands-on projects spanning image classification, sentiment analysis, and predictive modelling for real-world applications.

Code-Along Interactive Sessions

Participate in instructor-led coding sessions with real-time error debugging and architecture design discussions to reinforce learning concepts.

Practical Model Tuning Competitions

Engage in collaborative hyperparameter optimisation challenges that simulate real-world model performance improvement scenarios.

Pre-trained Model Implementation

Learn to leverage transfer learning with pre-trained architectures like ResNet and BERT for efficient model development and deployment.

End-to-End Neural Network Implementation

Build neural networks from scratch and develop complete machine learning pipelines from data preparation to model deployment.

Who Can Join Our Deep Learning Course in Dubai?

Data Scientists and Analysts

Professionals with data analysis experience seeking to advance into neural network implementation.

Machine Learning Engineers

Engineers wanting to enhance capabilities with deep learning architectures and optimisation strategies.

Software Developers

Python programmers interested in integrating AI capabilities into applications or specialised roles.

AI Researchers

Academic or industry researchers implementing cutting-edge neural network architectures and techniques.

Technical Leads and Product Managers

Decision-makers require a comprehensive understanding of deep learning for organisational implementation.

Automation Specialists

Professionals aiming to leverage deep learning for intelligent process optimisation systems.

Deep Learning Course Modules

Module 1: Neural Networks and Deep Learning
  • Chapter 1.1: Fundamentals of Neural Networks

    • Lesson 1.1.1: What are Neural Networks?
    • Lesson 1.1.2: Why Deep Learning?
  • Chapter 1.2: Core Components of Neural Networks

    • Lesson 1.2.1: Forward Propagation
    • Lesson 1.2.2: Cost Function
    • Lesson 1.2.3: Backpropagation
    • Lesson 1.2.4: Activation Functions
  • Chapter 1.3: Building Neural Networks

    • Lesson 1.3.1: Building Shallow Networks
    • Lesson 1.3.2: Deep Networks
    • Lesson 1.3.3: Overfitting and Underfitting
  • Chapter 1.4: Practical Considerations

    • Lesson 1.4.1: Initialization Techniques
    • Lesson 1.4.2: Gradient Issues in Training Neural Networks
Module 2: Improving Deep Neural Networks – Hyperparameter Tuning, Regularisation and Optimisation
  • Chapter 2.1: Regularisation Techniques

    • Lesson 2.1.1: L2 Regularization
    • Lesson 2.1.2: Dropout
  • Chapter 2.2: Optimisation Algorithms

    • Lesson 2.2.1: Gradient Descent Variants
    • Lesson 2.2.2: Momentum
    • Lesson 2.2.3: RMSProp
    • Lesson 2.2.4: Adam
  • Chapter 2.3: Training Improvements

    • Lesson 2.3.1: Batch Normalization
    • Lesson 2.3.2: Learning Rate Schedules
    • Lesson 2.3.3: Practical Tricks for Stable and Faster Training
  • Chapter 2.4: Hyperparameter Tuning

    • Lesson 2.4.1: Setting Hyperparameters
    • Lesson 2.4.2: Tuning Strategies and Best Practices
Module 3: Structuring Machine Learning Projects
  • Chapter 3.1: Diagnosing Model Performance

    • Lesson 3.1.1: Understanding Bias vs. Variance
    • Lesson 3.1.2: Making Decisions on What to Improve
  • Chapter 3.2: Data Splitting and Management

    • Lesson 3.2.1: Setting Train/Validation/Test Splits Correctly
    • Lesson 3.2.2: Using Data Effectively in Project Design
  • Chapter 3.3: Advanced Project Strategies

    • Lesson 3.3.1: Transfer Learning
    • Lesson 3.3.2: ML System Design Principles
  • Chapter 3.4: Real-World Applications

    • Lesson 3.4.1: Case Studies in ML and Deep Learning Project Design
Module 4: Convolutional Neural Networks (CNNs)
  • Chapter 4.1: Core CNN Concepts

    • Lesson 4.1.1: Convolutions
    • Lesson 4.1.2: Pooling
  • Chapter 4.2: Building CNN Architectures

    • Lesson 4.2.1: Designing CNNs
    • Lesson 4.2.2: Training CNNs
  • Chapter 4.3: Computer Vision Applications

    • Lesson 4.3.1: Visual Recognition Tasks
    • Lesson 4.3.2: Object Detection
    • Lesson 4.3.3: Image Segmentation
  • Chapter 4.4: Advanced CNN Techniques

    • Lesson 4.4.1: Using Pre-trained Models and Transfer Learning
    • Lesson 4.4.2: Advanced Architectures (e.g., ResNet)
Module 5: Sequence Models
  • Chapter 5.1: Recurrent Neural Networks (RNNs)

    • Lesson 5.1.1: Understanding RNNs
    • Lesson 5.1.2: LSTMs
    • Lesson 5.1.3: GRUs
  • Chapter 5.2: Advanced Sequence Modeling

    • Lesson 5.2.1: Sequence-to-Sequence Models
    • Lesson 5.2.2: Attention Mechanisms
    • Lesson 5.2.3: Basics of Transformers
  • Chapter 5.3: Real-World Sequence Applications

    • Lesson 5.3.1: Applications in Natural Language Processing (NLP)
    • Lesson 5.3.2: Applications in Speech and Other Sequence Data

Download Deep Learning Course Brochure

Real-World Projects and Lab Activities in Our Deep Learning Course in Dubai

Our Deep Learning Course in Dubai offers hands-on labs and projects to build neural networks and solve real-world AI problems using TensorFlow and PyTorch, including:

Projects

  • Development Environment Setup

    Configure Google Colab with TensorFlow or PyTorch to establish your professional deep learning workspace.

  • Neural Network Implementation from Scratch

    Build a 2-layer neural network without high-level libraries to understand fundamental architecture components.

  • Optimisation and Regularisation Techniques

    Apply dropout and L2 regularisation on the CIFAR-10 dataset to improve model performance and prevent overfitting.

  • Convolutional Neural Networks for Image Classification

    Develop a CNN from scratch to classify images while understanding filter operations and feature extraction.

  • Sequence Modelling with LSTM

    Create an LSTM model to predict stock price sequences using time-series data and recurrent architectures.

  • Real-World Deep Learning Application

    Build and present a full deep learning solution using real-world data in Vision, NLP, or Time Series domains.

Deep Learning Course Outcome and Career Opportunities in Dubai

​Our Deep Learning Training Course in Dubai provides comprehensive outcomes and opens up numerous career opportunities, including:​

Course Outcome Image
Gain the ability to build, train, and optimise Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) using TensorFlow and PyTorch.
Develop and deploy AI solutions for image classification, object detection, text processing, and time-series prediction through hands-on lab projects and a capstone assignment.
Master techniques such as dropout, L2 regularisation, and learning rate scheduling to prevent overfitting and improve model generalisation.
Learn to use advanced evaluation metrics and validation methods to measure, analyse, and enhance deep learning model accuracy and performance.
Utilise modern architectures like ResNet, BERT, and Transformer-based models to accelerate model training and improve outcomes on complex tasks.
Build an end-to-end project portfolio showcasing your expertise in deep learning, positioning yourself for high-demand roles such as Deep Learning Engineer or Computer Vision Specialist.

Career Opportunities After Completing the Deep Learning Course in Dubai

  • Computer Vision Engineer
  • NLP Specialist
  • AI Solutions Architect
  • Machine Learning Engineer
  • Lead AI Engineer

Companies Hiring Deep Learning Professionals in Dubai

  • Capgemini
  • Emaratech
  • Peyk
  • Deloitte
  • AirNxt
  • Printerpix
  • Digipay
  • Dicetek LLC
  • Deeplight
  • Tether Operations Limited

Deep Learning Training Options

Classroom Training

  • 40 Hours Deep Learning Training in Dubai

  • Hands-On Neural Network Implementation

  • Interactive Architecture Design Discussions

  • Real-Time Model Tuning Sessions

  • Small Group Collaborative Learning

Online Training

  • 40 Hours Deep Learning Online Training

  • Flexible Schedule for Working Professionals

  • Live Coding Demonstrations

  • Virtual Lab Environment Access

Corporate Training

  • Flexible 5-Day Intensive Deep Learning Training

  • Customised Curriculum for Company Needs

  • Team-Based Project Implementation

  • Training delivered at a selected hotel, client premises, or Edoxi

  • Fly-Me-a-Trainer Option

Do You Want a Customised Training for Deep Learning?

Get expert assistance in getting you Deep Learning Course customised!

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Pay your course fees in four easy installments with Tabby.

How to Get a Deep Learning Certification in Dubai?

Here’s a four-step guide to becoming a certified Deep Learning professional.

Do You Want to be a Certified Professional in Deep Learning?

Join Edoxi’s Deep Learning Course

Why Choose Edoxi for the Deep Learning Course in Dubai?

Among many options available in Dubai, Edoxi is one of the best choices. Here’s why Edoxi’s Deep Learning training in Dubai is the perfect fit for your needs:

KHDA-Approved Quality

As a KHDA-approved training provider, our deep learning course meets Dubai's stringent educational standards. This ensures high-quality instruction that adheres to recognised benchmarks.

Industry Expert Trainers

Our instructors have implemented neural network solutions across multinational corporations throughout Dubai, bringing real-world expertise directly to students.

Data Science and Analytics Learning Pathway

Edoxi offers comprehensive progression from foundation to advanced level courses in data science and analytics, providing a complete educational journey for AI professionals.

Trusted Provider of Corporate AI Training

We deliver customised deep learning programs to technology companies, financial institutions and government entities across the Middle East, adapting to organisational requirements.

Small-Group Learning Environment

Our limited class sizes ensure personalised guidance with detailed feedback on your neural network implementations and AI project development throughout the course.

Global Presence with Strategic Locations

Edoxi operates across London, the UAE, Qatar and Kuwait, enriching our curriculum with international perspectives while maintaining relevance to local markets.

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Edoxi is Recommended by 95% of our Students

Meet Our Mentor

Our mentors are leaders and experts in their fields. They can challenge and guide you on your road to success!

mentor-image

Shahista Tabassum

Shahista Tabassum is an experienced Data Analyst with over 14 years of combined industry and training experience. She has successfully trained more than 2000 students in data analytics, Python programming, and data visualisation. Her career spans hands-on experience in multinational corporations, followed by 10 years of dedicated training, complemented by an M.E. in Web Technologies that strengthens her technical foundation. This dual perspective enables her to deliver real-world context alongside theoretical knowledge in Python programming, data science, statistical analysis, machine learning, and database management.

Shahista's project-based teaching methodology draws directly from her MNC experience, incorporating actual business scenarios and industry challenges into the classroom. Her approach emphasises practical application and data storytelling techniques used in corporate boardrooms, ensuring students learn not just technical skills but also how to communicate insights effectively to stakeholders. Through clear explanations and real-world case studies from her corporate tenure, she guides learners in building portfolio-worthy projects that demonstrate genuine business value, preparing them to contribute immediately in professional settings.

FAQ

Will I be able to build practical AI systems after completing this Deep Learning course in Dubai?

Yes, you will develop the skills to build end-to-end deep learning systems for image classification, natural language processing, and predictive analytics. The Deep Learning course includes multiple real-world projects that form a professional portfolio demonstrating your implementation capabilities.

What job roles can I pursue after completing the Deep Learning training in Dubai?

Completing the Deep Learning training in Dubai, you can pursue roles such as Deep Learning Engineer, AI Developer, Computer Vision Engineer, or NLP Specialist. These positions are highly sought after across the UAE’s industries, including technology, healthcare, and finance.

What is the expected salary after completing the Deep Learning training in Dubai?

Professionals completing this Deep Learning course can expect starting salaries ranging from 12,000 to 25,000 AED per month in the UAE, depending on experience, specialisation, and the hiring organisation.

What projects will I complete during this Deep Learning course in Dubai?

You will complete a capstone project involving a full deep learning pipeline using real-world data in Vision, NLP, or Time Series, including data preprocessing, model training, evaluation, and presentation.

What frameworks and tools will we use during the Deep Learning training in Dubai?

You will work extensively with TensorFlow and PyTorch, the industry's leading deep learning frameworks. Additional tools include Jupyter Notebooks, Google Colab, NumPy, Pandas, and specialised libraries like Hugging Face Transformers for NLP applications.