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:
- Understand the fundamentals of deep learning and neural networks
- Explain how deep learning differs from traditional machine learning
- Identify key components such as layers, neurons, and activation functions
- Recognize common deep learning architectures (CNNs, RNNs, etc.)
- Understand how deep learning models are trained and evaluated
- 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
- What is deep learning
- Evolution from machine learning to deep learning
- Key use cases and applications
Module 2: Neural Network Fundamentals
- Structure of artificial neural networks
- Neurons, layers, and connections
- Activation functions
Module 3: Training Deep Learning Models
- Forward and backward propagation
- Loss functions and optimization
- Overfitting and model evaluation
Module 4: Deep Learning Architectures
- Introduction to Convolutional Neural Networks (CNNs)
- Introduction to Recurrent Neural Networks (RNNs)
- Overview of modern architectures (Transformers)
Module 5: Tools and Frameworks
- Overview of popular frameworks (TensorFlow, PyTorch)
- Basic workflow for building models
- Demonstration of a simple model
Module 6: Applications of Deep Learning
- Computer vision use cases
- Natural language processing
- Speech and recommendation systems
Module 7: Challenges and Future Trends
- Data requirements and computational needs
- Ethical considerations
- Emerging trends in deep learning
Training Material Provided
- Presentation slides and conceptual diagrams
- Sample notebooks and basic code examples
- Case studies and real-world examples
- Reference guide for deep learning concepts
