Neural Networks - A Primer

Overview

Explore neural networks' foundations, intuition, and applications in machine learning. Learn about their historical inspiration, mathematical concepts, and practical tools for developing AI solutions.

Syllabus

• Introduction to Neural Networks
Overview of Neural Networks
Historical Inspirations and Biological Analogs
Importance in Machine Learning
• Fundamental Concepts of Neural Networks
Neurons and Perceptrons
The Architecture of Neural Networks
Activation Functions
• Mathematical Foundations
Linear Algebra Essentials
Building Blocks: Weights, Biases, and Layers
The Role of Differentiation and Gradient Descent
• Training Neural Networks
Forward and Backward Propagation
Loss Functions and Optimization Techniques
Regularization Methods
• Popular Neural Network Architectures
Feedforward Neural Networks
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
• Tools and Libraries for Neural Network Development
Overview of Popular Frameworks (TensorFlow, PyTorch)
Building a Simple Neural Network with Python
Hands-on Project: Image Classification
• Applications of Neural Networks
Computer Vision
Natural Language Processing
Reinforcement Learning
• Challenges and Limitations
Overfitting and Underfitting
Interpretability and Explainability
Ethical Considerations in AI Solutions
• Future Directions and Trends in Neural Networks
Emerging Architectures
Integrating Neural Networks with Other AI Technologies
• Course Review and Next Steps
Recap of Key Concepts
Recommended Resources for Further Learning
Final Project: Develop Your Own Neural Network Solution

Subjects

Conference Talks