From a single neuron to a network that learns
A story-driven, from-first-principles walk through neural networks — starting with a single artificial neuron and ending with a fully trained multi-layer perceptron, backpropagation, optimizers, and everything in between.
1. What Is Artificial Intelligence, Really?
Separating the hype from the actual idea
2. Machine Learning Fundamentals
Teaching computers by example instead of by rule
3. Deep Learning Fundamentals
What makes 'deep' learning different
4. A Brief History of Neural Networks
Two winters, one thaw, and a revolution
5. Biological vs. Artificial Neurons
How much of the brain analogy is actually true?
9. Linear Algebra for Neural Networks
Vectors, matrices, and why networks are just matrix math
10. Calculus for Neural Networks
Derivatives, gradients, and the chain rule that makes learning possible
11. Probability & Statistics Essentials
Making sense of uncertainty in data and predictions
12. Optimization Theory
How a network actually searches for good weights
13. MLP Architecture
Layers, neurons, and how information flows forward
14. Backpropagation
How a network learns from its own mistakes
15. Activation Functions
The spark that lets networks learn anything non-linear
16. Loss Functions
Teaching a network what 'wrong' means
17. Optimizers
Smarter ways to walk downhill
18. Weight Initialization
Why the starting point of training matters so much
19. Regularization
Keeping a network from memorizing instead of learning
20. The Training Pipeline
Putting every piece together into one training loop
21. Practical Implementation
Building an MLP for real, bugs and all
22. Real-World Applications
Where MLPs actually show up in production
23. MLPs Compared to Other Architectures
When to reach for an MLP, and when not to
24. Exam & Interview Prep
The questions that come up again and again