Neural Networks - A Primer

Overview

Explore neural networks' fundamentals, from biological inspiration to practical applications. Learn key concepts, mathematical foundations, and tools for developing machine learning solutions using neural networks.

Syllabus

• Introduction to Neural Networks
Overview and historical context
Biological inspiration and analogy to human neurons
• Mathematical Foundations
Neurons as mathematical models
Activation functions (sigmoid, ReLU, etc.)
Loss functions and optimization
• Types of Neural Networks
Feedforward neural networks
Convolutional neural networks (CNNs)
Recurrent neural networks (RNNs)
• Neural Network Architecture
Layers and nodes
Weight initialization and bias
Backpropagation and gradient descent
• Practical Implementation
Setting up a development environment (Python, TensorFlow, PyTorch)
Building simple neural networks
Training and evaluating models
• Advanced Topics
Regularization techniques (dropout, L2 normalization)
Hyperparameter tuning
Transfer learning
• Real-world Applications
Image classification and object detection
Natural language processing
Time-series prediction
• Ethical Considerations and Future Trends
Bias and fairness in AI
The future of neural networks and AI advancements
• Resources and Further Learning
Key textbooks and papers
Online courses and tutorials
• Project Work
Develop a simple neural network application from scratch
Present findings and lessons learned

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Subjects

Conference Talks