# Deep Learning

> Join Edoxi's 40-hour online Deep Learning course. Master neural networks, build AI systems with TensorFlow & PyTorch, and earn your Deep Learning certification.

## Course Details
- Rating: 4.9/5 (100 reviews)
- Category: Software & Technology
- Sub-Category: Emerging Technology

## Course Introduction
Edoxi’s 40-hour Online Deep Learning training develops advanced AI skills for data scientists and AI enthusiasts. Learn neural networks, CNN, RNN, and ANN architectures, and apply practical AI solutions. Gain hands-on experience with TensorFlow and PyTorch and master deep architecture design, optimisation, and model evaluation. Work on real-world projects in image classification, NLP, and time-series forecasting. Enrol to prepare for high-demand roles and lead AI innovation.

## Course Overview
- Delivery Modes: Online
- Course Duration: 40 Hours
- Corporate Days: 5 Days
- Learners Enrolled: 50+
- Modules: 5

## What Do You Learn from Edoxi's Deep Learning Training

**Deep Learning Foundations**
Gain a solid understanding of the mathematical concepts behind neural networks and backpropagation. Learn gradient descent methods and regularisation techniques to optimise model learning.

**CNN, RNN, and ANN Architectures**
Explore major neural network architectures and their applications. Practise implementing these models using TensorFlow and PyTorch.

**Computer Vision Applications**
Build image classification and object detection models using convolutional networks. Apply transfer learning with pre-trained models to solve real-world visual challenges.

**Deep Architecture Design**
Design multi-layer deep learning models with proper weight initialisation and structured connectivity. Create effective architectures for complex tasks.

**Activation Functions and Optimisation**
Understand key activation functions such as sigmoid, ReLU, tanh, and advanced variants. Learn how to choose the right function and optimiser to improve model performance.

**Model Evaluation Techniques**
Use a range of evaluation metrics to measure model accuracy and effectiveness. Learn validation, testing, and optimisation methods to improve overall model performance.

## About This Course
## About Our Online Deep Learning Course 

Edoxi’s 40-hour online Deep Learning course equips data scientists and AI enthusiasts with essential skills in neural network architectures and practical AI applications.​ Our Deep Learning training focuses on hands-on learning with TensorFlow and PyTorch, helping participants build advanced AI systems for image, text, and data analysis.

The Deep Learning course curriculum covers fundamental mathematical principles behind neural networks, advanced architectures such as CNNs, RNNs, and ANNs, and optimisation techniques. Participants gain practical experience in computer vision, deep network design, and model evaluation through interactive sessions and projects. Key course modules include Neural Networks, Deep Learning, CNNs, Sequence Models, and ML Project Structuring.

Upon successful completion of our Deep Learning course, you will gain A course completion certificate from Edoxi. This credential validates your skills in high-demand roles like Deep Learning Engineer, Computer Vision Engineer, and AI Solutions Architect, with strong career growth potential in the global AI sector. 

Enrol now to advance your career in Deep Learning and AI.

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## Key Features of Edoxi's Deep Learning Training

**Hands-on TensorFlow & PyTorch**
Work in full lab environments to build neural networks and implement deep learning models using industry-standard frameworks.

**Complete Deep Learning Project Portfolio**
Create a professional portfolio with projects in image classification, sentiment analysis, and predictive modelling for real-world use cases.

**Code-Along Interactive Sessions**
Join instructor-led coding sessions featuring real-time debugging, architecture walkthroughs, and guided implementation.

**Practical Model Tuning Challenges**
Participate in hyperparameter optimisation exercises that simulate real-world model improvement scenarios.

**Pre-trained Model Integration**
Apply transfer learning using architectures like ResNet and BERT to accelerate model development and deployment.

**End-to-End Neural Network Development**
Build neural networks from scratch and implement full ML workflows, from data preparation to model deployment.

## Who Can Join Our Online Online Deep Learning Course

**Data Scientists and Analysts**
Professionals with data experience looking to move into neural network development.

**Machine Learning Engineers**
Engineers aiming to strengthen their skills in deep learning architectures and optimisation techniques.

**Software Developers**
Python developers interested in adding AI features to applications or transitioning into specialised AI roles.

**AI Researchers**
Those in academia or industry working on advanced neural network models and emerging techniques.

**Technical Leads and Product Managers**
Decision-makers who need a solid understanding of deep learning to guide organisational AI initiatives.

**Automation Specialists**
Professionals seeking to use deep learning for smart automation and intelligent process optimisation.

## 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

## Hands-On Lab Activities

**Environment Setup**
Set up Google Colab with TensorFlow or PyTorch to create a complete deep learning workspace.

**Neural Network from Scratch**
Build a simple 2-layer neural network without high-level libraries to understand core architecture concepts.

**Optimisation & Regularisation**
Use dropout and L2 regularisation on the CIFAR-10 dataset to boost accuracy and reduce overfitting.

**CNN for Image Classification**
Build a CNN from the ground up to classify images and learn how filters and feature extraction work.

**LSTM for Sequence Modelling**
Develop an LSTM model to forecast stock price trends using time-series data.

**End-to-End Real Project**
Create and present a full deep learning solution using real data in computer vision, NLP, or time-series forecasting.

## Deep Learning Course Outcome and Career Opportunities
Our Deep Learning Training Course delivers exciting career opportunities and strong learning outcomes, including:

- Build, train, and optimise ANN, CNN, and RNN models using industry-standard frameworks like TensorFlow and PyTorch.
- Develop and deploy AI solutions for image classification, object detection, NLP tasks, and time-series forecasting through hands-on labs and a capstone project.
- Master key optimisation techniques—dropout, L2 regularisation, and learning-rate scheduling, to reduce overfitting and enhance model generalisation.
- Apply advanced evaluation metrics and validation approaches to accurately analyse and improve model performance.
- Work with modern architectures such as ResNet, BERT, and Transformer-based models to achieve high-quality results on complex datasets.
- Build a complete project portfolio that demonstrates your deep learning expertise and prepares you for roles like Deep Learning Engineer, AI Specialist, and Computer Vision Expert.

## Career Opportunities After Completing the Deep Learning Training
Computer Vision Engineer, NLP Specialist, AI Solutions Architect, Machine Learning Engineer, Lead AI Engineer

## Deep Learning Training Options

**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

## How to Get a Deep Learning Certification?
Here’s a four-step guide to becoming a certified Deep Learning professional.

1. Join Edoxi’s Deep Learning Certification Course.
2. Attend our Expert-led Deep Learning Training.
3. Complete the Deep Learning Classes.
4. Earn your Deep Learning Certification.

## Why Choose Edoxi for the Online Deep Learning Course?
Among the many options available, Edoxi is one of the top choices for professional training. Here’s why Edoxi’s Deep Learning training is the best fit for your needs:


**Expert Trainers with Real-World Experience**
Our instructors bring hands-on skills from implementing neural network solutions across major organisations.

**Structured Data Science Learning Pathway**
Edoxi offers a complete progression from beginner to advanced data science and analytics programs, supporting long-term career growth for AI professionals.

**Preferred Corporate AI Training Partner**
We design customised deep learning programs for tech companies, financial institutions, and government entities, tailored to organisational needs.

**Small, Focused Learning Groups**
Our limited class sizes ensure personalised attention, detailed feedback, and close instructor support for your deep learning projects.

**Global Training Presence**
With centres in London, the UAE, Qatar, and Kuwait, Edoxi combines global insights with local market relevance to enhance the learning experience.

**Course Completion Certificate**
Upon successfully completing the Deep Learning course, you will receive a Course Completion Certificate from Edoxi.

## Frequently Asked Questions

**Q: Will I be able to build practical AI systems after completing this Deep Learning course?**
A: Yes. You will gain the skills to build end-to-end deep learning systems for image classification, natural language processing, and predictive analytics. The course includes multiple real-world projects that help you build a strong professional portfolio.

**Q: What salary can I expect after completing the Deep Learning training?**
A: After completing Deep Learning training, beginners usually earn $80,000 to $130,000 a year. The salary depends on factors such as the company, location, and your skill level.

**Q: What job roles can I pursue after completing the Deep Learning training?**
A: After completing the training, you can pursue roles such as Deep Learning Engineer, AI Developer, Computer Vision Engineer, and NLP Specialist, positions in high demand across industries like tech, healthcare, finance, and more.

**Q: How is Deep Learning different from traditional Machine Learning?**
A: Deep Learning uses multi-layer neural networks that automatically learn features from data, while traditional ML often relies on manual feature engineering. The course explains these differences clearly and covers when to use each approach.

**Q: How hands-on is this Deep Learning course?**
A: The course is highly practical, with most sessions focused on implementation. You will complete 20+ hands-on exercises, from building neural networks from scratch to working with advanced architectures for real-world applications.

## Trainer
- Name: Shahista Tabassum
- Designation: Senior IT Technical Trainer
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.

## Enrol in This Course
- Course URL: https://www.edoxi.com/deep-learning-course
- Phone: +971 43801666
- Email: info@edoxi.com