Description:

This course provides a foundational introduction to deep learning, a subset of artificial intelligence focused on neural networks and data-driven learning. Participants will explore how deep learning models work, understand key architectures, and learn how these models are applied in real-world scenarios such as image recognition, natural language processing, and speech analysis.

Through conceptual explanations and basic demonstrations, learners will gain a clear understanding of how deep learning differs from traditional machine learning and how it powers modern AI applications.

Duration:

1Day

Course Code: BDT66

Learning Objectives:

After this course, you will be able to:

1. Understand the fundamentals of deep learning and neural networks

2. Explain how deep learning differs from traditional machine learning

3. Identify key components such as layers, neurons, and activation functions

4. Recognize common deep learning architectures (CNNs, RNNs, etc.)

5. Understand how deep learning models are trained and evaluated

6. Explore real-world applications of deep learning

Beginners in AI/ML, students, data analysts, and developers interested in deep learning concepts

 Basic knowledge of Python and fundamental machine learning concepts

Course Outline:

Module 1: Introduction to Deep Learning

1. What is deep learning
2. Evolution from machine learning to deep learning
3. Key use cases and applications

Module 2: Neural Network Fundamentals

1. Structure of artificial neural networks
2. Neurons, layers, and connections
3. Activation functions

Module 3: Training Deep Learning Models

1. Forward and backward propagation
2. Loss functions and optimization
3. Overfitting and model evaluation

Module 4: Deep Learning Architectures

1. Introduction to Convolutional Neural Networks (CNNs)
2. Introduction to Recurrent Neural Networks (RNNs)
3. Overview of modern architectures (Transformers)

Module 5: Tools and Frameworks

1. Overview of popular frameworks (TensorFlow, PyTorch)
2. Basic workflow for building models
3. Demonstration of a simple model

Module 6: Applications of Deep Learning

1. Computer vision use cases
2. Natural language processing
3. Speech and recommendation systems

Module 7: Challenges and Future Trends

1. Data requirements and computational needs
2. Ethical considerations
3. Emerging trends in deep learning

Training Material Provided

1. Presentation slides and conceptual diagrams
2. Sample notebooks and basic code examples
3. Case studies and real-world examples
4. Reference guide for deep learning concepts