DeepLearning From Scratch

Rebuilding the foundations of modern AI — from first principles.

This repository is a long-term, structured effort to deeply understand the core ideas that power modern Artificial Intelligence.
Every architecture implemented here is rebuilt from the ground up with minimal abstraction, focusing on mathematical clarity, algorithmic understanding, and implementation-level intuition.

The goal is not just to make models work — but to understand why they work.

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Philosophy

Modern deep learning frameworks make it easy to use powerful models.
This repository exists to understand them.

Each implementation follows three principles:

1. First Principles — Derivations before code.
2. Minimal Abstraction — No unnecessary high-level shortcuts.
3. Clarity Over Convenience — Readability and conceptual transparency come first.

This is a study of the mechanics behind deep learning — gradients, optimization, architectures, and the mathematical structure underlying them.

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What This Repository Contains

• Re-implementations of influential deep learning papers
• Mathematical derivations and conceptual explanations
• Clean, modular implementations of core architectures
• Structured experiments and notebooks for deeper exploration

Over time, this repository will grow into a comprehensive collection of:

• Foundational models (MLP, CNN, RNN, Transformers, etc.)
• Optimization techniques
• Regularization methods
• Architectural innovations
• Key research milestones in deep learning

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Repository Structure

DeepLearning-From-Scratch/
│
├── architectures/      # Model implementations with explanations
├── notebooks/          # Experimental explorations and walkthroughs
├── docs/               # Mathematical derivations and conceptual notes (planned)
├── requirements.txt
└── README.md

Each architecture directory contains:

• Clean implementation
• Explanation of the core idea
• References to the original paper

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Why This Exists

• To develop intuition beyond surface-level usage.
• To bridge theory and implementation.
• To build research-level understanding of AI systems.
• To document the journey of mastering deep learning from the ground up.

This repository is both:

• A personal laboratory for serious study
• A public portfolio of disciplined, principle-driven learning

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Getting Started

1. Clone the repository

git clone https://github.com/aniketmishr/DeepLearning-From-Scratch.git
cd DeepLearning-From-Scratch

2. Install dependencies

pip install -r requirements.txt

3. Explore

Start with the architectures/ directory to examine implementations. Use the notebooks/ directory for guided experimentation.

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Long-Term Vision

This is not a short-term project. It is an evolving body of work aimed at mastering the foundations of intelligent systems.

The ambition is simple:

Understand deep learning — not as a user, but as a builder.

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License

This project is open-source under the MIT License.