Build-Deep-Learning-From-Scratch
NewA project-based curriculum that teaches PyTorch by rebuilding it from scratch. Start with scalar backpropagation, create a reverse-mode autodiff engine, expand it into tensors, then implement neural networks, optimizers, CNNs, attention, Transformers, ViTs, and a mini PyTorch framework. Every gradient is code you write and understand.
Summary
This skill guides you through building a deep learning framework from scratch, mirroring PyTorch's core components.
- You'll implement autodiff, tensors, neural network layers, optimizers, and advanced architectures like Transformers, gaining deep understanding of every gradient and operation.
Install & Usage
mkdir -p .claude/skillsAdd the configuration to .claude/skills/build-deep-learning-from-scratch.md
/build-deep-learning-from-scratchUse Cases
Usage Examples
/build-deep-learning-from-scratch implement a scalar autograd engine with tanh activation
Help me build a 2-layer neural network from scratch using the autograd engine I just created
Implement a Transformer encoder block with multi-head attention and feed-forward network
Security Audits
Frequently Asked Questions
What is Build-Deep-Learning-From-Scratch?
This skill guides you through building a deep learning framework from scratch, mirroring PyTorch's core components. You'll implement autodiff, tensors, neural network layers, optimizers, and advanced architectures like Transformers, gaining deep understanding of every gradient and operation.
How to install Build-Deep-Learning-From-Scratch?
To install Build-Deep-Learning-From-Scratch: create the skills directory (mkdir -p .claude/skills), then add the config to .claude/skills/build-deep-learning-from-scratch.md. Finally, /build-deep-learning-from-scratch in Claude Code.
What is Build-Deep-Learning-From-Scratch best for?
Build-Deep-Learning-From-Scratch is a other categorized under General. Created by roiamiel1.
What can I use Build-Deep-Learning-From-Scratch for?
Build-Deep-Learning-From-Scratch is useful for: Learn reverse-mode automatic differentiation by implementing a scalar-level autograd engine.; Build tensor operations and expand the autograd engine to support multi-dimensional arrays.; Implement neural network layers (Linear, Conv2d, etc.) and optimizers (SGD, Adam) from scratch.; Construct a mini Transformer model with multi-head attention and positional encoding.; Develop a Vision Transformer (ViT) for image classification tasks.; Create a complete mini PyTorch framework with module system, data loaders, and training loops..