Understanding Neural Networks from Scratch: A Deep Learning Notes Repository with C++ Implementation

Project Basic Information

• Original Author/Maintainer: cameraman-pritam
• Source Platform: GitHub
• Project Link: https://github.com/cameraman-pritam/ann-notes-obsidian
• Release Date: May 30, 2026

This project combines an Obsidian notes repository with a C++ neural network engine, aiming to help learners deeply understand the underlying mechanisms of neural networks from first principles. It fills the gap where developers rely on high-level APIs but lack knowledge of mathematical principles and system implementation. Covering full-link content such as theoretical derivation, C++ architecture, and cross-platform deployment, it is suitable for technical learners who want to know not only the 'what' but also the 'why'.

Today, with the popularity of deep learning frameworks, most developers are used to calling high-level APIs of PyTorch/TensorFlow, but lack understanding of the mathematical principles and system implementation behind these tools. This project helps learners master the underlying logic of neural networks through a combination of theory and practice, filling this cognitive gap.

Perceptrons and Dimensional Contracts

• Dot Product Operation: The dot product of the weight vector and input vector determines the neuron activation state
• Dimensional Matching: Constraint relationships between input, hidden, and output layers
• Bias Term Function: Key parameter for adjusting neuron activation thresholds

Backpropagation and Chain Rule

• Chain Rule: Composite function derivative rule for error propagation from the output layer back to the input layer
• Gradient Calculation: Impact of weight parameter gradients on network performance
• Gradient Issues: Gradient vanishing/explosion in deep networks and mitigation solutions (e.g., ReLU replacing Sigmoid)

Memory Management and Data Structures

• std::vector Contiguous Memory: Suitable for storing neural network weights
• Memory Layout Optimization: Improves efficiency of large-scale matrix operations
• mt19937 Pseudorandom Numbers: Ensures symmetry breaking and reproducibility in weight initialization

Data Parsing and Binary I/O

• CSV Parser: Reads training data line by line
• Binary Model Files: Defines byte-level storage format for weights, biases, and layer structures
• Cross-Platform Compatibility: Ensures model portability across different architectures
• Fast Loading: Binary format has better performance than text format

WebAssembly Frontend Deployment

1. Emscripten toolchain compiles C++ to .wasm files
2. Generate JS glue code
3. React frontend directly calls Wasm modules
4. HTML canvas data local prediction Advantages: Privacy protection, low latency, no GPU server costs

REST API Backend Deployment

• Containerize as Docker image
• Deploy to Render/Fly.io serverless platforms
• Provide standardized RESTful API interfaces Supports three modes: native binary, Wasm, cloud services

Recommended Learning Path

1. Theoretical Foundation: Read mathematical derivations in Obsidian notes
2. Code Implementation: Follow C++ code to build a multi-layer perceptron
3. System Optimization: Study memory management and parallel computing techniques
4. Deployment Practice: Try Wasm or REST API deployment

Target Audience

• Computer Science Students: Deepen understanding of deep learning principles
• Embedded Developers: Deploy on resource-constrained devices
• Algorithm Engineers: Optimize model performance bottlenecks
• Tech Enthusiasts: Explore AI underlying implementation

Technical Highlights

• First-Principles Teaching: Build from scratch without relying on existing frameworks
• Dual Perspective: Mathematical abstraction + underlying implementation
• Production-Ready: Industrial-grade code quality
• Multi-Platform Support: Native, Wasm, cloud services
• Complete Toolchain: Full process from data parsing to inference

Conclusion

This project represents a back-to-basics learning approach. Implementing neural networks from scratch deepens technical understanding, cultivates engineering thinking, and provides learners with a clear path for advancement.