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The Architect
Neural Networks
The Human Brain in the Context of Graph Theory
- Intuition Behind AI w.r.t Graph Theory Derived from Human Brain Neurons
- Understanding how neural networks mimic biological systems
- Graph-based representations of neural connections
Applying Graph Theory to Build a Two-Layer Neural Network (Model-2)
- Analyzing the Signature Patterns of Each Concept Space in a Two-Layer Neural Network
- Designing network architectures using graph principles
- Understanding information flow through network layers
The Importance of Starting with Negative Weights (Claiming the Boundary of a Concept Space)
- Building a 2-Layer Neural Network
- Strategic weight initialization for better learning
- Understanding decision boundaries in neural networks
Analyzing the Computational Complexities of All the 3 Models
- Understanding the 'cost' of building and running AI models
- Performance trade-offs in different network architectures
- Optimization strategies for efficient computation
Hands-on Python Code Exercises
- Implementing and experimenting with your two-layer neural network
- Building networks from scratch using fundamental operations
- Testing different architectures and comparing results