Neural Networks Notes and Implementations

Concise, practical notes and minimal implementations across perceptrons, multilayer perceptrons, and convolutional neural networks. High-level "Concepts" offer quick intuition, while "Internal-details.md" captures deeper derivations, math, and design trade-offs.

Repository Overview

This repository is organized by model family. Each folder contains two documentation layers to aid both quick revision and deep study:

• Concepts (notes.md): a bird's-eye narrative of what and why for each topic
• Internal-details.md: deeper mechanics, derivations, and edge cases

What Each Part Contains

ANNs and Perceptrons

• notes.md: high-level concepts, linear decision boundaries, step vs. sigmoid activations, and perceptron convergence intuition
• Internal-details.md: margin, separability assumptions, perceptron updates and loss variants, connections to logistic regression
• .py files: training loop with mistake-driven updates and toy-data evaluation

Multilayer Perceptron Architecture

• notes.md: intuition for depth and hidden layers, nonlinearity (ReLU, tanh), overfitting risks, and common training recipes
• Internal-details.md: forward/backprop equations, parameter shapes, initialization, regularization (L2, dropout)

Convolutional Neural Networks

• notes.md: CNN intuition—locality, weight sharing, receptive fields, pooling—and when CNNs are preferable
• Internal-details.md: convolution/pooling operators, padding/stride math, feature-map sizing, and common architectural patterns

Getting Started

Prerequisites

• Python 3.9+
• NumPy
• Matplotlib/Seaborn
• scikit-learn for toy datasets

Suggested Reading Flow

1. Skim notes.md for intuition
2. Study Internal-details.md for mechanics
3. Run any provided minimal implementation

Future Additions

• Add requirements.txt for reproducibility
• Consider "Open in Colab" badges as the repository grows

Learning Goals

• Build an intuition-first map of core neural architectures for quick recall
• Maintain deeper derivations in Internal-details.md to support rigorous understanding and future expansion

Contributing

• Open issues or pull requests for fixes, clarifications, or new minimal examples
• Keep notes concise and runnable code small
• Follow a simple style: one concept per paragraph, short code blocks, and targeted figures where helpful

Roadmap

• Add minimal MLP example with training loop
• Add small CNN demo (e.g., MNIST-like)
• Add requirements.txt
• Add optional badges (Python version, license)

License

This project is licensed under the MIT License - see the LICENSE file for details.

Documentation Style

• Keep README as the entry point answering what, why, and how
• Place deeper derivations per topic in Internal-details.md
• Use clear Markdown headings, lists, and fenced code blocks
• Update the overview section when new folders/examples are added

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Note: This repository serves as a personal learning log and reference guide for neural network fundamentals. Feel free to use, modify, and contribute to improve the documentation and examples.